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Deina C, Fogliatto FS, da Silveira GJC, Anzanello MJ. Decision analysis framework for predicting no-shows to appointments using machine learning algorithms. BMC Health Serv Res 2024; 24:37. [PMID: 38183029 PMCID: PMC10770919 DOI: 10.1186/s12913-023-10418-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/30/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources. METHODS In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%. RESULTS From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets. CONCLUSION This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.
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Affiliation(s)
- Carolina Deina
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° Andar, Porto Alegre, 90035-190, Brazil.
| | - Flavio S Fogliatto
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° Andar, Porto Alegre, 90035-190, Brazil
| | - Giovani J C da Silveira
- Haskayne School of Business, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada
| | - Michel J Anzanello
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° Andar, Porto Alegre, 90035-190, Brazil
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Shour AR, Jones GL, Anguzu R, Doi SA, Onitilo AA. Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system. BMC Health Serv Res 2023; 23:989. [PMID: 37710258 PMCID: PMC10503036 DOI: 10.1186/s12913-023-09969-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/25/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND No-show appointments pose a significant challenge for healthcare providers, particularly in rural areas. In this study, we developed an evidence-based predictive model for patient no-shows at the Marshfield Clinic Health System (MCHS) rural provider network in Wisconsin, with the aim of improving overbooking approaches in outpatient settings and reducing the negative impact of no-shows in our underserved rural patient populations. METHODS Retrospective data (2021) were obtained from the MCHS scheduling system, which included 1,260,083 total appointments from 263,464 patients, as well as their demographic, appointment, and insurance information. We used descriptive statistics to associate variables with show or no-show status, logistic regression, and random forests utilized, and eXtreme Gradient Boosting (XGBoost) was chosen to develop the final model, determine cut-offs, and evaluate performance. We also used the model to predict future no-shows for appointments from 2022 and onwards. RESULTS The no-show rate was 6.0% in both the train and test datasets. The train and test datasets both yielded 5.98. Appointments scheduled further in advance (> 60 days of lead time) had a higher (7.7%) no-show rate. Appointments for patients aged 21-30 had the highest no-show rate (11.8%), and those for patients over 60 years of age had the lowest (2.9%). The model predictions yielded an Area Under Curve (AUC) of 0.84 for the train set and 0.83 for the test set. With the cut-off set to 0.4, the sensitivity was 0.71 and the positive predictive value was 0.18. Model results were used to recommend 1 overbook for every 6 at-risk appointments per provider per day. CONCLUSIONS Our findings demonstrate the feasibility of developing a predictive model based on administrative data from a predominantly rural healthcare system. Our new model distinguished between show and no-show appointments with high performance, and 1 overbook was advised for every 6 at-risk appointments. This data-driven approach to mitigating the impact of no-shows increases treatment availability in rural areas by overbooking appointment slots on days with an elevated risk of no-shows.
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Affiliation(s)
- Abdul R Shour
- Cancer Care and Research Center, Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, WI, USA
| | - Garrett L Jones
- Information Technology and Digital Services Analytics, Gundersen Health System, Marshfield, WI, USA
| | - Ronald Anguzu
- Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Suhail A Doi
- Department of Population Medicine, College of Medicine, Qatar University, Doha, Qatar
| | - Adedayo A Onitilo
- Cancer Care and Research Center, Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, WI, USA.
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3
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Borges A, Carvalho M, Maia M, Guimarães M, Carneiro D. Predicting and explaining absenteeism risk in hospital patients before and during COVID-19. SOCIO-ECONOMIC PLANNING SCIENCES 2023; 87:101549. [PMID: 37255583 PMCID: PMC9972778 DOI: 10.1016/j.seps.2023.101549] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 02/15/2023] [Accepted: 02/22/2023] [Indexed: 06/01/2023]
Abstract
In order to address one of the most challenging problems in hospital management - patients' absenteeism without prior notice - this study analyses the risk factors associated with this event. To this end, through real data from a hospital located in the North of Portugal, a prediction model previously validated in the literature is used to infer absenteeism risk factors, and an explainable model is proposed, based on a modified CART algorithm. The latter intends to generate a human-interpretable explanation for patient absenteeism, and its implementation is described in detail. Furthermore, given the significant impact, the COVID-19 pandemic had on hospital management, a comparison between patients' profiles upon absenteeism before and during the COVID-19 pandemic situation is performed. Results obtained differ between hospital specialities and time periods meaning that patient profiles on absenteeism change during pandemic periods and within specialities.
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Affiliation(s)
- Ana Borges
- CIICESI, ESTG, Politecnico do Porto, Rua do Curral, Casa do Curral, Margaride, Felgueiras, 4610-156, Portugal
| | - Mariana Carvalho
- CIICESI, ESTG, Politecnico do Porto, Rua do Curral, Casa do Curral, Margaride, Felgueiras, 4610-156, Portugal
| | - Miguel Maia
- CIICESI, ESTG, Politecnico do Porto, Rua do Curral, Casa do Curral, Margaride, Felgueiras, 4610-156, Portugal
| | - Miguel Guimarães
- CIICESI, ESTG, Politecnico do Porto, Rua do Curral, Casa do Curral, Margaride, Felgueiras, 4610-156, Portugal
| | - Davide Carneiro
- CIICESI, ESTG, Politecnico do Porto, Rua do Curral, Casa do Curral, Margaride, Felgueiras, 4610-156, Portugal
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Taheri-Shirazi M, Namdar K, Ling K, Karmali K, McCradden MD, Lee W, Khalvati F. Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning. Front Public Health 2023; 11:968319. [PMID: 36908403 PMCID: PMC9998668 DOI: 10.3389/fpubh.2023.968319] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 01/30/2023] [Indexed: 03/14/2023] Open
Abstract
In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features include age, sex, income, distance from the hospital, percentage of non-English speakers in a postal code, percentage of single caregivers in a postal code, appointment time slot (morning, afternoon, evening), and day of the week (Monday to Sunday). We trained univariate Logistic Regression (LR) models using the training sets and identified predictive (significant) features that remained significant in the test sets. We also implemented multivariate Random Forest (RF) models to predict the endpoints. We achieved Area Under the Receiver Operating Characteristic Curve (AUC) of 0.82 and 0.73 for predicting no-show and long waiting room time endpoints, respectively. The univariate LR analysis on DI appointments uncovered the effect of the time of appointment during the day/week, and patients' demographics such as income and the number of caregivers on the no-shows and long waiting room time endpoints. For predicting no-show, we found age, time slot, and percentage of single caregiver to be the most critical contributors. Age, distance, and percentage of non-English speakers were the most important features for our long waiting room time prediction models. We found no sex discrimination among the scheduled pediatric DI appointments. Nonetheless, inequities based on patient features such as low income and language barrier did exist.
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Affiliation(s)
- Maryam Taheri-Shirazi
- Department of Diagnostic Imaging, The Hospital for Sick Children (SickKids), Toronto, ON, Canada
| | - Khashayar Namdar
- Department of Diagnostic Imaging, The Hospital for Sick Children (SickKids), Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada.,NVIDIA Deep Learning Institute, Austin, TX, United States
| | - Kelvin Ling
- Department of Diagnostic Imaging, The Hospital for Sick Children (SickKids), Toronto, ON, Canada
| | - Karima Karmali
- Department of Diagnostic Imaging, The Hospital for Sick Children (SickKids), Toronto, ON, Canada
| | - Melissa D McCradden
- Department of Bioethics, The Hospital for Sick Children (SickKids), Toronto, ON, Canada.,Peter Giligan Centre for Research and Learning - Genetics and Genome Biology Program, Toronto, ON, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Wayne Lee
- Department of Diagnostic Imaging, The Hospital for Sick Children (SickKids), Toronto, ON, Canada
| | - Farzad Khalvati
- Department of Diagnostic Imaging, The Hospital for Sick Children (SickKids), Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada.,Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.,Department of Computer Science, University of Toronto, Toronto, ON, Canada
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Babayoff O, Shehory O, Geller S, Shitrit-Niselbaum C, Weiss-Meilik A, Sprecher E. Improving Hospital Outpatient Clinics Appointment Schedules by Prediction Models. J Med Syst 2022; 47:5. [PMID: 36585996 DOI: 10.1007/s10916-022-01902-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 12/14/2022] [Indexed: 01/01/2023]
Abstract
Patient no-shows and suboptimal patient appointment length scheduling reduce clinical efficiency and impair the clinic's quality of service. The main objective of this study is to improve appointment scheduling in hospital outpatient clinics. We developed generic supervised machine learning models to predict patient no-shows and patient's length of appointment (LOA). We performed a retrospective study using more than 100,000 records of patient appointments in a hospital outpatient clinic. Several machine learning algorithms were used for the development of our prediction models. We trained our models on a dataset that contained patients', physicians', and appointments' characteristics. Our feature set combines both unstudied features and features adopted from previous studies. In addition, we identified the influential features for predicting LOA and no-show. Our LOA model's performance was 6.92 in terms of MAE, and our no-show model's performance was 92.1% in terms of F-score. We compared our models' performance to the performance of previous research models by applying their methods to our dataset; our models demonstrated better performance. We show that the major effector of such differences is the use of our novel features. To evaluate the effect of our prediction results on the quality of schedules produced by appointment systems (AS), we developed an interface layer between our prediction models and the AS, where prediction results comprise the AS input. Using our prediction models, there was an 80% improvement in the daily cumulative patient waiting time and a 33% reduction in the daily cumulative physician idle time.
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Affiliation(s)
| | - Onn Shehory
- Bar-Ilan University, 5290002, Ramat Gan, Israel
| | - Shamir Geller
- Division of Dermatology, Tel Aviv Sourasky Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chen Shitrit-Niselbaum
- I-Medata AI Center, Tel-Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Ahuva Weiss-Meilik
- I-Medata AI Center, Tel-Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Eli Sprecher
- Division of Dermatology, Tel Aviv Sourasky Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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Pan M, Yang Q, Su T, Geng K, Liang K. An effective tremor-filtering model in teleoperation: Three-domain Wavelet Least Square Support Vector Machine. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Abed-alguni BH, Alawad NA, Al-Betar MA, Paul D. Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection. APPL INTELL 2022; 53:13224-13260. [PMID: 36247211 PMCID: PMC9547101 DOI: 10.1007/s10489-022-04201-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2022] [Indexed: 12/03/2022]
Abstract
This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. FS is an essential machine learning and data mining task of choosing a subset of highly discriminating features from noisy, irrelevant, high-dimensional, and redundant features to best represent a dataset. SCA is a recent metaheuristic algorithm established to emulate a model based on sine and cosine trigonometric functions. It was initially proposed to tackle problems in the continuous domain. The SCA has been modified to Binary SCA (BSCA) to deal with the binary domain of the FS problem. To improve the performance of BSCA, three accumulative improved variations are proposed (i.e., IBSCA1, IBSCA2, and IBSCA3) where the last version has the best performance. IBSCA1 employs Opposition Based Learning (OBL) to help ensure a diverse population of candidate solutions. IBSCA2 improves IBSCA1 by adding Variable Neighborhood Search (VNS) and Laplace distribution to support several mutation methods. IBSCA3 improves IBSCA2 by optimizing the best candidate solution using Refraction Learning (RL), a novel OBL approach based on light refraction. For performance evaluation, 19 real-wold datasets, including a COVID-19 dataset, were selected with different numbers of features, classes, and instances. Three performance measurements have been used to test the IBSCA versions: classification accuracy, number of features, and fitness values. Furthermore, the performance of the last variation of IBSCA3 is compared against 28 existing popular algorithms. Interestingly, IBCSA3 outperformed almost all comparative methods in terms of classification accuracy and fitness values. At the same time, it was ranked 15 out of 19 in terms of number of features. The overall simulation and statistical results indicate that IBSCA3 performs better than the other algorithms.
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Affiliation(s)
| | | | - Mohammed Azmi Al-Betar
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
| | - David Paul
- School of Science and Technology, University of New England, Armidale, Australia
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8
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Balakrishnan K, Dhanalakshmi R, Akila M, Sinha BB. Improved equilibrium optimization based on Levy flight approach for feature selection. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09461-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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9
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Xue Y, Cai X, Neri F. A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109420] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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10
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Self-adaptive opposition-based differential evolution with subpopulation strategy for numerical and engineering optimization problems. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00734-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractOpposition-based differential evolution (ODE) is a well-known DE variant that employs opposition-based learning (OBL) to accelerate the convergence speed. However, the existing OBL variants are population-based, which causes many shortcomings. The value of the jumping rate is not self-adaptively adjusted, so the algorithm easily traps into local optima. The population-based OBL wastes fitness evaluations when the algorithm converges to sub-optimal. In this paper, we proposed a novel OBL called subpopulation-based OBL (SPOBL) with a self-adaptive parameter control strategy. In SPOBL, the jumping rate acts on the individual, and the subpopulation is selected according to the individual’s jumping rate. In the self-adaptive parameter control strategy, the surviving individual’s jumping rate in each iteration will participate in the self-adaptive process. A generalized Lehmer mean is introduced to achieve an equilibrium between exploration and exploitation. We used DE and advanced DE variants combined with SPOBL to verify performance. The results of performance are evaluated on the CEC 2017 and CEC 2020 test suites. The SPOBL shows better performance compared to other OBL variants in terms of benchmark functions as well as real-world constrained optimization problems.
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11
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Multi-objective feature selection based on quasi-oppositional based Jaya algorithm for microarray data. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107804] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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12
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A self-adaptive weighted differential evolution approach for large-scale feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107633] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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13
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Qiu C, Liu N. A novel three layer particle swarm optimization for feature selection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-202647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Feature selection (FS) is a vital data preprocessing task which aims at selecting a small subset of features while maintaining a high level of classification accuracy. FS is a challenging optimization problem due to the large search space and the existence of local optimal solutions. Particle swarm optimization (PSO) is a promising technique in selecting optimal feature subset due to its rapid convergence speed and global search ability. But PSO suffers from stagnation or premature convergence in complex FS problems. In this paper, a novel three layer PSO (TLPSO) is proposed for solving FS problem. In the TLPSO, the particles in the swarm are divided into three layers according to their evolution status and particles in different layers are treated differently to fully investigate their potential. Instead of learning from those historical best positions, the TLPSO uses a random learning exemplar selection strategy to enrich the searching behavior of the swarm and enhance the population diversity. Further, a local search operator based on the Gaussian distribution is performed on the elite particles to improve the exploitation ability. Therefore, TLPSO is able to keep a balance between population diversity and convergence speed. Extensive comparisons with seven state-of-the-art meta-heuristic based FS methods are conducted on 18 datasets. The experimental results demonstrate the competitive and reliable performance of TLPSO in terms of improving the classification accuracy and reducing the number of features.
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Affiliation(s)
- Chenye Qiu
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
| | - Ning Liu
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
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14
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Optimization of process parameters for turning of titanium alloy (Grade II) in MQL environment using multi-CI algorithm. SN APPLIED SCIENCES 2021. [DOI: 10.1007/s42452-021-04197-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
AbstractThe advancement of materials science during the last few decades has led to the development of many hard-to-machine materials, such as titanium, stainless steel, high-strength temperature-resistant alloys, ceramics, refractories, fibre-reinforced composites, and superalloys. Titanium is a prominent material and widely used for several industrial applications. However, it has poor machinability and hence efficient machining is critical. Machining of titanium alloy (Grade II) in minimum quantity lubrication (MQL) environment is one of the recent approaches towards sustainable manufacturing. This problem has been solved using various approaches such as experimental investigation, desirability, and with optimization algorithms. In the group of socio-inspired optimization algorithm, an artificial intelligence (AI)-based methodology referred to as Cohort Intelligence (CI) has been developed. In this paper, CI algorithm and Multi-CI algorithm have been applied for optimizing process parameters associated with turning of titanium alloy (Grade II) in MQL environment. The performance of these algorithms is exceedingly better as compared with particle swarm optimization algorithm, experimental and desirability approaches. The analysis regarding the convergence and run time of all the algorithms is also discussed. It is important to mention that for turning of titanium alloy in MQL environment, Multi-CI achieved 8% minimization of cutting force, 42% minimization of tool wear, 38% minimization of tool-chip contact length, and 15% minimization of surface roughness when compared with PSO. For desirability and experimental approaches, 12% and 8% minimization of cutting force, 42% and 47% minimization of tool wear, 53% and 40% minimization of tool-chip contact length, and 15% and 20% minimization of surface roughness were attained, respectively.
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Radhika A, Masood MS. Effective dimensionality reduction by using soft computing method in data mining techniques. Soft comput 2021. [DOI: 10.1007/s00500-020-05474-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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16
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Rasheed I, Banka H, Khan HM. A Hybrid Feature Selection Approach Based on LSI for Classification of Urdu Text. STUDIES IN COMPUTATIONAL INTELLIGENCE 2021. [DOI: 10.1007/978-3-030-50641-4_1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Bonakdari H, Pelletier JP, Martel-Pelletier J. A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events. J Transl Med 2020; 18:466. [PMID: 33298067 PMCID: PMC7724897 DOI: 10.1186/s12967-020-02628-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/20/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND An important task in developing accurate public health intervention evaluation methods based on historical interrupted time series (ITS) records is to determine the exact lag time between pre- and post-intervention. We propose a novel continuous transitional data-driven hybrid methodology using a non-linear approach based on a combination of stochastic and artificial intelligence methods that facilitate the evaluation of ITS data without knowledge of lag time. Understanding the influence of implemented intervention on outcome(s) is imperative for decision makers in order to manage health systems accurately and in a timely manner. METHODS To validate a developed hybrid model, we used, as an example, a published dataset based on a real health problem on the effects of the Italian smoking ban in public spaces on hospital admissions for acute coronary events. We employed a continuous methodology based on data preprocessing to identify linear and nonlinear components in which autoregressive moving average and generalized structure group method of data handling were combined to model stochastic and nonlinear components of ITS. We analyzed the rate of admission for acute coronary events from January 2002 to November 2006 using this new data-driven hybrid methodology that allowed for long-term outcome prediction. RESULTS Our results showed the Pearson correlation coefficient of the proposed combined transitional data-driven model exhibited an average of 17.74% enhancement from the single stochastic model and 2.05% from the nonlinear model. In addition, data demonstrated that the developed model improved the mean absolute percentage error and correlation coefficient values for which 2.77% and 0.89 were found compared to 4.02% and 0.76, respectively. Importantly, this model does not use any predefined lag time between pre- and post-intervention. CONCLUSIONS Most of the previous studies employed the linear regression and considered a lag time to interpret the impact of intervention on public health outcome. The proposed hybrid methodology improved ITS prediction from conventional methods and could be used as a reliable alternative in public health intervention evaluation.
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Affiliation(s)
- Hossein Bonakdari
- Osteoarthritis Research Unit, University of Montreal Hospital Research Centre (CRCHUM), 900 Saint-Denis Street, R11.412, Montreal, QC, H2X 0A9, Canada.,Department of Soil and Agri-Food Engineering, Laval University, 2425 rue de l'Agriculture, Québec, QC, G1V 0A6, Canada
| | - Jean-Pierre Pelletier
- Osteoarthritis Research Unit, University of Montreal Hospital Research Centre (CRCHUM), 900 Saint-Denis Street, R11.412, Montreal, QC, H2X 0A9, Canada
| | - Johanne Martel-Pelletier
- Osteoarthritis Research Unit, University of Montreal Hospital Research Centre (CRCHUM), 900 Saint-Denis Street, R11.412, Montreal, QC, H2X 0A9, Canada.
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18
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Detection of anomaly intrusion utilizing self-adaptive grasshopper optimization algorithm. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05500-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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kelidari M, Hamidzadeh J. Feature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operator. Soft comput 2020. [DOI: 10.1007/s00500-020-05349-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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20
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Patil MV, Kulkarni AJ. Pareto dominance based Multiobjective Cohort Intelligence algorithm. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.05.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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21
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Qiu C. A hybrid two-stage feature selection method based on differential evolution. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Chenye Qiu
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China
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22
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Carreras-García D, Delgado-Gómez D, Llorente-Fernández F, Arribas-Gil A. Patient No-Show Prediction: A Systematic Literature Review. ENTROPY 2020; 22:e22060675. [PMID: 33286447 PMCID: PMC7517206 DOI: 10.3390/e22060675] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 06/13/2020] [Accepted: 06/14/2020] [Indexed: 12/02/2022]
Abstract
Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.
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Affiliation(s)
- Danae Carreras-García
- Department of Statistics, University Carlos III of Madrid, 28911 Leganés, Spain; (D.C.-G.); (F.L.-F.)
| | - David Delgado-Gómez
- Department of Statistics, University Carlos III of Madrid, 28911 Leganés, Spain; (D.C.-G.); (F.L.-F.)
- UC3M-Santander Big Data Institute, University Carlos III of Madrid, 28903 Getafe, Spain;
- Correspondence:
| | | | - Ana Arribas-Gil
- UC3M-Santander Big Data Institute, University Carlos III of Madrid, 28903 Getafe, Spain;
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