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Colombo T, Mangone M, Agostini F, Bernetti A, Paoloni M, Santilli V, Palagi L. Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis. PLoS One 2021; 16:e0261511. [PMID: 34941924 PMCID: PMC8699618 DOI: 10.1371/journal.pone.0261511] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 12/05/2021] [Indexed: 11/18/2022] Open
Abstract
The aim of our study was to classify scoliosis compared to to healthy patients using non-invasive surface acquisition via Video-raster-stereography, without prior knowledge of radiographic data. Data acquisitions were made using Rasterstereography; unsupervised learning was adopted for clustering and supervised learning was used for prediction model Support Vector Machine and Deep Network architectures were compared. A M-fold cross validation procedure was performed to evaluate the results. The accuracy and balanced accuracy of the best supervised model were close to 85%. Classification rates by class were measured using the confusion matrix, giving a low percentage of unclassified patients. Rasterstereography has turned out to be a good tool to distinguish subject with scoliosis from healthy patients limiting the exposure to unnecessary radiations.
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Affiliation(s)
- Tommaso Colombo
- Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
- aHead Research ETS, Rome, Italy
| | - Massimiliano Mangone
- Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Francesco Agostini
- Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Andrea Bernetti
- Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Marco Paoloni
- Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Valter Santilli
- Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Laura Palagi
- Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
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Zhang Y, Wei X, Cao C, Yu F, Li W, Zhao G, Wei H, Zhang F, Meng P, Sun S, Lammi MJ, Guo X. Identifying discriminative features for diagnosis of Kashin-Beck disease among adolescents. BMC Musculoskelet Disord 2021; 22:801. [PMID: 34537022 PMCID: PMC8449456 DOI: 10.1186/s12891-021-04514-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 07/07/2021] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Diagnosing Kashin-Beck disease (KBD) involves damages to multiple joints and carries variable clinical symptoms, posing great challenge to the diagnosis of KBD for clinical practitioners. However, it is still unclear which clinical features of KBD are more informative for the diagnosis of Kashin-Beck disease among adolescent. METHODS We first manually extracted 26 possible features including clinical manifestations, and pathological changes of X-ray images from 400 KBD and 400 non-KBD adolescents. With such features, we performed four classification methods, i.e., random forest algorithms (RFA), artificial neural networks (ANNs), support vector machines (SVMs) and linear regression (LR) with four feature selection methods, i.e., RFA, minimum redundancy maximum relevance (mRMR), support vector machine recursive feature elimination (SVM-RFE) and Relief. The performance of diagnosis of KBD with respect to different classification models were evaluated by sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). RESULTS Our results demonstrated that the 10 out of 26 discriminative features were displayed more powerful performance, regardless of the chosen of classification models and feature selection methods. These ten discriminative features were distal end of phalanges alterations, metaphysis alterations and carpals alterations and clinical manifestations of ankle joint movement limitation, enlarged finger joints, flexion of the distal part of fingers, elbow joint movement limitation, squatting limitation, deformed finger joints, wrist joint movement limitation. CONCLUSIONS The selected ten discriminative features could provide a fast, effective diagnostic standard for KBD adolescents.
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Affiliation(s)
- Yanan Zhang
- School of Public Health, Xi'an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People's Republic of China, Xi'an, Shaanxi, P.R. China
| | - Xiaoli Wei
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China
| | - Chunxia Cao
- Institute of Disaster Medicine, Tianjin University, Tianjin, P.R. China
| | - Fangfang Yu
- Department of Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, P. R. China
| | - Wenrong Li
- School of Public Health, Xi'an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People's Republic of China, Xi'an, Shaanxi, P.R. China
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Guanghui Zhao
- Xi'an Honghui Hospital, Health Science Center of Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China
| | - Haiyan Wei
- School of Public Health, Xi'an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People's Republic of China, Xi'an, Shaanxi, P.R. China
| | - Feng'e Zhang
- School of Public Health, Xi'an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People's Republic of China, Xi'an, Shaanxi, P.R. China
| | - Peilin Meng
- School of Public Health, Xi'an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People's Republic of China, Xi'an, Shaanxi, P.R. China
| | - Shiquan Sun
- School of Public Health, Xi'an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People's Republic of China, Xi'an, Shaanxi, P.R. China
| | - Mikko Juhani Lammi
- School of Public Health, Xi'an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People's Republic of China, Xi'an, Shaanxi, P.R. China.
- Department of Integrative Medical Biology, University of Umeå, 90187, Umeå, Sweden.
| | - Xiong Guo
- School of Public Health, Xi'an Jiaotong University, Key Laboratory of Trace Elements and Endemic Diseases, National Health Commission of the People's Republic of China, Xi'an, Shaanxi, P.R. China.
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Shahraki AD, Safdari R, Gahfarokhi HH, Tahmasebian S. The Usage of Association Rule Mining to Identify Influencing Factors on Deafness After Birth. Acta Inform Med 2015; 23:356-9. [PMID: 26862245 PMCID: PMC4720831 DOI: 10.5455/aim.2015.23.356-359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Accepted: 11/17/2015] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND Providing complete and high quality health care services has very important role to enable people to understand the factors related to personal and social health and to make decision regarding choice of suitable healthy behaviors in order to achieve healthy life. For this reason, demographic and clinical data of person are collecting, this huge volume of data can be known as a valuable resource for analyzing, exploring and discovering valuable information and communication. This study using forum rules techniques in the data mining has tried to identify the affecting factors on hearing loss after birth in Iran. MATERIALS AND METHODS The survey is kind of data oriented study. The population of the study is contained questionnaires in several provinces of the country. First, all data of questionnaire was implemented in the form of information table in Software SQL Server and followed by Data Entry using written software of C # .Net, then algorithm Association in SQL Server Data Tools software and Clementine software was implemented to determine the rules and hidden patterns in the gathered data. FINDINGS Two factors of number of deaf brothers and the degree of consanguinity of the parents have a significant impact on severity of deafness of individuals. Also, when the severity of hearing loss is greater than or equal to moderately severe hearing loss, people use hearing aids and Men are also less interested in the use of hearing aids. CONCLUSION In fact, it can be said that in families with consanguineous marriage of parents that are from first degree (girl/boy cousins) and 2(nd) degree relatives (girl/boy cousins) and especially from first degree, the number of people with severe hearing loss or deafness are more and in the use of hearing aids, gender of the patient is more important than the severity of the hearing loss.
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Affiliation(s)
| | - Reza Safdari
- Isfahan University of Medical Sciences, Isfahan, Iran
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Automatic classification of epilepsy types using ontology-based and genetics-based machine learning. Artif Intell Med 2014; 61:79-88. [DOI: 10.1016/j.artmed.2014.03.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2013] [Revised: 02/24/2014] [Accepted: 03/07/2014] [Indexed: 11/21/2022]
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Espejo P, Ventura S, Herrera F. A Survey on the Application of Genetic Programming to Classification. ACTA ACUST UNITED AC 2010. [DOI: 10.1109/tsmcc.2009.2033566] [Citation(s) in RCA: 379] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Liew PL, Lee YC, Lin YC, Lee TS, Lee WJ, Wang W, Chien CW. Comparison of artificial neural networks with logistic regression in prediction of gallbladder disease among obese patients. Dig Liver Dis 2007; 39:356-62. [PMID: 17317348 DOI: 10.1016/j.dld.2007.01.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2006] [Revised: 12/29/2006] [Accepted: 01/10/2007] [Indexed: 12/11/2022]
Abstract
BACKGROUND Obesity is a risk factor for gallbladder disease. The authors retrospectively analyse the prevalence and risk factors of gallbladder disease using logistic regression and artificial neural networks among obese patients in Taiwan. METHODS Artificial neural networks is a popular technique, which can detect complex patterns within data. They have not been applied to risk of gallbladder disease in obese population. We studied the risk factors associated with gallstones in 117 obese patients who were undergoing bariatric surgery between February 1999 and October 2005. Artificial neural networks, constructed with three-layered back-propagation algorithm, were trained to predict the risk of gallbladder disease. Thirty input variables including clinical data (gender, age, body mass index and associated diseases), laboratory evaluation and histopathologic findings of gallbladder were obtained from the patient records. The result was compared with a logistic regression model developed from the same database. RESULTS Artificial neural networks demonstrated better average classification rate and lower Type II errors than those of logistic regression. The risk factors from both data mining techniques were diastolic blood pressure, inflammatory condition, abnormal glucose metabolism and cholesterolosis. The biological significance of inflammatory condition in obese population requires further investigation. CONCLUSION Artificial neural networks might be a useful tool to predict the risk factors and prevalence of gallbladder disease and gallstone development in obese patients on the basis of multiple variables related to laboratory and pathological features. The performance of artificial neural networks was better than traditional modeling techniques.
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Affiliation(s)
- P-L Liew
- Department of Pathology, En-Chu Kong Hospital, Taipei Hsien, Taiwan.
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Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms. Ecol Modell 2007. [DOI: 10.1016/j.ecolmodel.2006.03.040] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Tsakonas A. A comparison of classification accuracy of four genetic programming-evolved intelligent structures. Inf Sci (N Y) 2006. [DOI: 10.1016/j.ins.2005.03.012] [Citation(s) in RCA: 65] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Kim KJ, Cho SB. A comprehensive overview of the applications of artificial life. ARTIFICIAL LIFE 2006; 12:153-82. [PMID: 16393455 DOI: 10.1162/106454606775186455] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.
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Affiliation(s)
- Kyung-Joong Kim
- Department of Computer Science, Yonsei University, 134 Shinchon-dong, Seoul 120-749, Korea.
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Paetz J. Finding optimal decision scores by evolutionary strategies. Artif Intell Med 2004; 32:85-95. [PMID: 15364093 DOI: 10.1016/j.artmed.2004.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2003] [Revised: 11/05/2003] [Accepted: 04/16/2004] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Severeness of illness is often rated by physicians at admission time. For this purpose, medical scores have been developed as 'objective' rating methods. When considering their classification performance, it is not assumed that such an expert-driven score is an optimal one. Our aim is to design an optimized data-driven score. In particular, we compare classical scores with a new data-driven score for abdominal septic shock patients. METHODS AND MATERIAL Medical scores are used as ratings for different aspects of a patient's health status. The medical score indicates either a more critical or a healthier condition. For example, physicians rate organ conditions for different organs. We consider four different scores, SOFA, APACHE II, SAPS II, and MODS. Beyond the use of such classical scores, we propose an evolutionary strategy, that is suitable for score design, to find optimized data-driven scores. A database of 282 patients is used to optimize a new score for abdominal septic shock patients. Classification performance is compared by a ROC analysis. RESULTS We give a general instruction for building optimized scores, i.e. we define individuals and operators for the evolutionary score design task. We apply this instruction to abdominal septic shock patient data. When compared to the SOFA score, it has similar classification performance, but it is more performant than APACHE II, SAPS II, and MODS. It can be used as a daily bedside score. CONCLUSIONS We argue that evolutionary strategies should be used for optimizing purposes in the medical score design process. Using abdominal septic shock patient data, we show that evolutionary score design is a feasible and performant method that can complement or replace expert knowledge, provided that qualitative data is available.
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Affiliation(s)
- Jürgen Paetz
- Fachbereich Biologie und Informatik, Institut für Informatik, J.W. Goethe-Universität Frankfurt am Main, Robert-Mayer-Strabetae 11-15, D-60054 Frankfurt am Main, Germany.
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O’Looney J. Sprawl decisions: A simulation and decision support tool for citizens and policy makers. GOVERNMENT INFORMATION QUARTERLY 2001. [DOI: 10.1016/s0740-624x(01)00088-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Abstract
The term evolutionary computation encompasses a host of methodologies inspired by natural evolution that are used to solve hard problems. This paper provides an overview of evolutionary computation as applied to problems in the medical domains. We begin by outlining the basic workings of six types of evolutionary algorithms: genetic algorithms, genetic programming, evolution strategies, evolutionary programming, classifier systems, and hybrid systems. We then describe how evolutionary algorithms are applied to solve medical problems, including diagnosis, prognosis, imaging, signal processing, planning, and scheduling. Finally, we provide an extensive bibliography, classified both according to the medical task addressed and according to the evolutionary technique used.
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Affiliation(s)
- C A Peña-Reyes
- Logic Systems Laboratory, Swiss Federal Institute of Technology, IN-Ecublens, CH-1015, Lausanne, Switzerland.
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