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Bairagi T, Jahid Hasan M, Hudha M, Azad A, Rahman M. Artificial neural network (ANN) analysis on thermophysical properties of magnetohydrodynamics flow with radiation in an arc-shaped enclosure with a rotating cylinder. Heliyon 2024; 10:e28609. [PMID: 38689950 PMCID: PMC11059520 DOI: 10.1016/j.heliyon.2024.e28609] [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] [Received: 11/13/2023] [Revised: 02/05/2024] [Accepted: 03/21/2024] [Indexed: 05/02/2024] Open
Abstract
The objective of this research is to examine the thermophysical features of magnetic parameter (Ha) and time step (τ) in a lid-driven cavity using a water-based Al2O3 nanofluid and the efficacy of ANN models in accurately predicting the average heat transfer rate. The Galerkin weighted residual approach is used to solve a set of dimensionless nonlinear governing equations. The Levenberg-Marquardt back propagation technique is used for training ANN using sparse simulated data. The findings of the investigation about the flow and thermal fields are shown. Furthermore, a comparative study and prediction have been conducted on the impact of manipulating factors on the average Nusselt number derived from the numerical heat transfer analysis. The findings of the research indicate that, in the absence of magnetohydrodynamics, a rise in the Hartmann number resulted in a drop in both the fluid velocity profile and magnitude. Conversely, it was observed that the temperature and Nusselt number exhibited an increase under these conditions. The mean temperature of the fluid rises as the Hartmann number drops, reaching a peak value of 0.114 when Ha = 0. The scenario where Ha = 0, representing the lack of magnetohydrodynamics, shows the highest average Nusselt number, whereas the instance with Ha = 45 presents the lowest Nusselt number. The ANN model has a high level of accuracy, as seen by an MSE value of 0.00069 and a MAE value of 0.0175, resulting in a 99% accuracy rate.
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Affiliation(s)
- T. Bairagi
- Department of Mathematics, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh
| | - Md. Jahid Hasan
- Department of Mechanical and Production Engineering, Islamic University of Technology, Board Bazar, Gazipur, 1704, Bangladesh
| | - M.N. Hudha
- Department of Mathematics, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh
| | - A.K. Azad
- Department of Natural Sciences, Islamic University of Technology, Board Bazar, Gazipur, 1704, Bangladesh
| | - M.M. Rahman
- Department of Mathematics, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh
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Ahmed SBS, Naeem S, Khan AMH, Qureshi BM, Hussain A, Aydogan B, Muhammad W. Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer. Front Artif Intell 2024; 7:1329737. [PMID: 38646416 PMCID: PMC11026659 DOI: 10.3389/frai.2024.1329737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/25/2024] [Indexed: 04/23/2024] Open
Abstract
Background and purpose We proposed an artificial neural network model to predict radiobiological parameters for the head and neck squamous cell carcinoma patients treated with radiation therapy. The model uses the tumor specification, demographics, and radiation dose distribution to predict the tumor control probability and the normal tissue complications probability. These indices are crucial for the assessment and clinical management of cancer patients during treatment planning. Methods Two publicly available datasets of 31 and 215 head and neck squamous cell carcinoma patients treated with conformal radiation therapy were selected. The demographics, tumor specifications, and radiation therapy treatment parameters were extracted from the datasets used as inputs for the training of perceptron. Radiobiological indices are calculated by open-source software using dosevolume histograms from radiation therapy treatment plans. Those indices were used as output in the training of a single-layer neural network. The distribution of data used for training, validation, and testing purposes was 70, 15, and 15%, respectively. Results The best performance of the neural network was noted at epoch number 32 with the mean squared error of 0.0465. The accuracy of the prediction of radiobiological indices by the artificial neural network in training, validation, and test phases were determined to be 0.89, 0.87, and 0.82, respectively. We also found that the percentage volume of parotid inside the planning target volume is the significant parameter for the prediction of normal tissue complications probability. Conclusion We believe that the model has significant potential to predict radiobiological indices and help clinicians in treatment plan evaluation and treatment management of head and neck squamous cell carcinoma patients.
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Affiliation(s)
- Saad Bin Saeed Ahmed
- Department of Physics, Florida Atlantic University, Boca Raton, FL, United States
| | - Shahzaib Naeem
- Gamma Knife Radiosurgery Center, Dow University of Health Sciences, Karachi, Pakistan
| | | | | | | | - Bulent Aydogan
- Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States
| | - Wazir Muhammad
- Department of Physics, Florida Atlantic University, Boca Raton, FL, United States
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Nisar KS, Anjum MW, Raja MAZ, Shoaib M. Recurrent neural network for the dynamics of Zika virus spreading. AIMS Public Health 2024; 11:432-458. [PMID: 39027393 PMCID: PMC11252581 DOI: 10.3934/publichealth.2024022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/06/2024] [Accepted: 03/13/2024] [Indexed: 07/20/2024] Open
Abstract
Recurrent Neural Networks (RNNs), a type of machine learning technique, have recently drawn a lot of interest in numerous fields, including epidemiology. Implementing public health interventions in the field of epidemiology depends on efficient modeling and outbreak prediction. Because RNNs can capture sequential dependencies in data, they have become highly effective tools in this field. In this paper, the use of RNNs in epidemic modeling is examined, with a focus on the extent to which they can handle the inherent temporal dynamics in the spread of diseases. The mathematical representation of epidemics requires taking time-dependent variables into account, such as the rate at which infections spread and the long-term effects of interventions. The goal of this study is to use an intelligent computing solution based on RNNs to provide numerical performances and interpretations for the SEIR nonlinear system based on the propagation of the Zika virus (SEIRS-PZV) model. The four patient dynamics, namely susceptible patients S(y), exposed patients admitted in a hospital E(y), the fraction of infective individuals I(y), and recovered patients R(y), are represented by the epidemic version of the nonlinear system, or the SEIR model. SEIRS-PZV is represented by ordinary differential equations (ODEs), which are then solved by the Adams method using the Mathematica software to generate a dataset. The dataset was used as an output for the RNN to train the model and examine results such as regressions, correlations, error histograms, etc. For RNN, we used 100% to train the model with 15 hidden layers and a delay of 2 seconds. The input for the RNN is a time series sequence from 0 to 5, with a step size of 0.05. In the end, we compared the approximated solution with the exact solution by plotting them on the same graph and generating the absolute error plot for each of the 4 cases of SEIRS-PZV. Predictions made by the model appeared to be become more accurate when the mean squared error (MSE) decreased. An increased fit to the observed data was suggested by this decrease in the MSE, which suggested that the variance between the model's predicted values and the actual values was dropping. A minimal absolute error almost equal to zero was obtained, which further supports the usefulness of the suggested strategy. A small absolute error shows the degree to which the model's predictions matches the ground truth values, thus indicating the level of accuracy and precision for the model's output.
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Affiliation(s)
- Kottakkaran Sooppy Nisar
- Department of Mathematics, College of Science and Humanities in Al Kharj, Prince Sattam bin Abdulaziz University, 11942, Saudi Arabia
- Saveetha School of Engineering, SIMATS, Chennai, India
| | | | - Muhammad Asif Zahoor Raja
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section .3, Douliou, Yunlin 64002, Taiwan, R.O.C
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Singh R, Pathak VK, Kumar R, Dikshit M, Aherwar A, Singh V, Singh T. A historical review and analysis on MOORA and its fuzzy extensions for different applications. Heliyon 2024; 10:e25453. [PMID: 38352792 PMCID: PMC10861981 DOI: 10.1016/j.heliyon.2024.e25453] [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] [Received: 10/21/2023] [Revised: 12/10/2023] [Accepted: 01/26/2024] [Indexed: 02/16/2024] Open
Abstract
Multi-criteria decision-making (MCDM) methods have been widely used among researchers to provide a trade-off solution between best and worst, considering conflicting criteria and sets of preferences. An efficient and systematic literature review of these methods is needed to maintain their application in distinctive domains. To this end, this paper presents a comprehensive and systematic literature survey on "multi-objective optimization by ratio analysis" (MOORA) method and its fuzzy extensions developed and discussed in recent years. This review includes articles categorized based on the publication name, publishing year, journal name, type of applications, and type of fuzzy extensions. In addition, this review will enhance the understanding of practitioners and decision-makers on the MOORA method, its development, fuzzy hybridization, different application areas, and future work. The study revealed that the MOORA technique was predominantly used with the TOPSIS approach, followed by the AHP and COPRAS methods. Furthermore, 76.28 % use single and hybridization approaches among all MOORA studies, while 23.72 % use MOORA in a fuzzy environment.
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Affiliation(s)
- Ramanpreet Singh
- Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
| | - Vimal Kumar Pathak
- Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
| | - Rakesh Kumar
- Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
| | - Mithilesh Dikshit
- Department of Mechanical & Aero-Space Engineering, Institute of Infrastructure, Technology, Research and Management, Ahmedabad, Gujarat, 380026, India
| | - Amit Aherwar
- Department of Mechanical Engineering, Madhav Institute of Technology and Science, Gwalior, 474005, India
| | - Vedant Singh
- Amrita School of Business, Amrita Vishwa Vidyapeetham, Bengaluru, 560035, India
| | - Tej Singh
- Savaria Institute of Technology, Faculty of Informatics, ELTE Eötvös Loránd University, Budapest 1117, Hungary
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Zhao X, Xiao C, Sun L, Zhang F. Assessment of left atrial function in patients with metabolic syndrome by four-dimensional automatic left atrial quantification. Diabetes Res Clin Pract 2024; 207:111080. [PMID: 38145827 DOI: 10.1016/j.diabres.2023.111080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 12/11/2023] [Accepted: 12/22/2023] [Indexed: 12/27/2023]
Abstract
OBJECTIVE This study aimed at assessing the changes of left atrial (LA) volume and strain function in metabolic syndrome (MS) patients using four-dimensional automatic left atrial quantification (4D-LAQ) and exploring independent correlative factors for LA function. METHODS A total of 110 MS patients and 70 normal controls were selected and assigned into the MS group and the control group, respectively. Echocardiogram parameters were routinely examined and the thickness of epicardial adipose tissue (EAT) were measured with a parasternal long axis of left ventricle(LV). The LA volume and strain parameters were determined using 4D-LAQ. The independent correlation factors for LA strain parameters in MS patients were investigated through linear regression analysis. RESULTS Compared with the control group, LA volume parameters were increased in the MS group, LA strain parameters and LA emptying fraction (LAEF) were decreased (all P < 0.05). EAT thickness is associated with LA reservoir longitudinal strain (LASr), conduit longitudinal strain (LAScd), reservoir circumferential strain (LASr-c), and conduit circumferential strain (LAScd-c) (all P < 0.05). LA contraction longitudinal (LASct) and circumferential strain (LASct-c) were not statistically significant. Regression analysis results show that systolic blood pressure (SBP) and triglyceride (TG) are independent correlative factors. Intra-observer and inter-observer repeatability test showed that the LA parameters examined by 4D-LAQ had good agreement. CONCLUSIONS 4D-LAQ is capable of effectively assessing the LA function in MS patients and providing a useful reference for clinical diagnosis. SBP and TG serve as the independent correlative factors for LA function.
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Affiliation(s)
- Xuebing Zhao
- Hebei Medical University, Shijiazhuang, Hebei, China; Department of Ultrasound, First Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
| | - Chengwei Xiao
- Hebei Medical University, Shijiazhuang, Hebei, China; Department of Ultrasound, First Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
| | - Lijuan Sun
- Department of Ultrasound, First Hospital of Qinhuangdao, Qinhuangdao, Hebei, China.
| | - Fang Zhang
- Department of Ultrasound, First Hospital of Qinhuangdao, Qinhuangdao, Hebei, China
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He M, Tang S, Xiao Y. Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic. PLoS Comput Biol 2023; 19:e1011535. [PMID: 37851640 PMCID: PMC10584194 DOI: 10.1371/journal.pcbi.1011535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 09/20/2023] [Indexed: 10/20/2023] Open
Abstract
During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi'an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.
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Affiliation(s)
- Mengqi He
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, China
| | - Sanyi Tang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, China
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
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Su X, Sun Y, Liu H, Lang Q, Zhang Y, Zhang J, Wang C, Chen Y. An innovative ensemble model based on deep learning for predicting COVID-19 infection. Sci Rep 2023; 13:12322. [PMID: 37516796 PMCID: PMC10387055 DOI: 10.1038/s41598-023-39408-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/25/2023] [Indexed: 07/31/2023] Open
Abstract
Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events.
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Affiliation(s)
- Xiaoying Su
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Yanfeng Sun
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Hongxi Liu
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Qiuling Lang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Yichen Zhang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
| | - Jiquan Zhang
- School of Environment, Northeast Normal University, Changchun, 130024, China
| | - Chaoyong Wang
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.
| | - Yanan Chen
- School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China
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Yassin H, Abo Elyazeed ER. Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non-extensive statistics. Sci Rep 2023; 13:10056. [PMID: 37344515 PMCID: PMC10284937 DOI: 10.1038/s41598-023-36959-8] [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: 01/03/2023] [Accepted: 06/13/2023] [Indexed: 06/23/2023] Open
Abstract
Non-extenstive statistics play a significant role in studying the dynamic behaviour of COVID-19 to assist epidemiological scientists to take appropriate decisions about pandemic planning. Generic non-extensive and modified-Tsallis statistics are used to analyze and predict the morbidity and mortality rates in future. The cumulative number of confirmed infection and death in Egypt at interval from 4 March 2020 till 12 April 2022 are analyzed using both non-extensive statistics. Also, the cumulative confirmed data of infection by gender, death by gender, and death by age in Egypt at interval from 4 March 2020 till 29 June 2021 are fitted using both statistics. The best fit parameters are estimated. Also, we study the dependence of the estimated fit parameters on the people gender and age. Using modified-Tsallis statistic, the predictions of the morbidity rate in female is more than the one in male while the mortality rate in male is greater than the one in female. But, within generic non-extensive statistic we notice that the gender has no effect on the rate of infections and deaths in Egypt. Then, we propose expressions for the dependence of the fitted parameters on the age. We conclude that the obtained fit parameters depend mostly on the age and on the type of the statistical approach applied and the mortality risk increased with people aged above 45 years. We predict - using modified-Tsallis - that the rate of infection and death in Egypt will begin to decrease till stopping during the first quarter of 2025.
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Affiliation(s)
- Hayam Yassin
- Physics Department, Faculty of Women for Arts, Science and Education, Ain Shams University, Cairo, 11577, Egypt.
| | - Eman R Abo Elyazeed
- Physics Department, Faculty of Women for Arts, Science and Education, Ain Shams University, Cairo, 11577, Egypt
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Li Y, Ma K. A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12528. [PMID: 36231828 PMCID: PMC9564883 DOI: 10.3390/ijerph191912528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/25/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
The coronavirus disease 2019 (COVID-19) has spread rapidly around the world since 2020, with a significant fatality rate. Until recently, numerous countries were unable to adequately control the pandemic. As a result, COVID-19 trend prediction has become a hot topic in academic circles. Both traditional models and existing deep learning (DL) models have the problem of low prediction accuracy. In this paper, we propose a hybrid model based on an improved Transformer and graph convolution network (GCN) for COVID-19 forecasting. The salient feature of the model in this paper is that rich temporal sequence information is extracted by the multi-head attention mechanism, and then the correlation of temporal sequence information is further aggregated by GCN. In addition, to solve the problem of the high time complexity of the existing Transformer, we use the cosine function to replace the softmax calculation, so that the calculation of query, key and value can be split, and the time complexity is reduced from the original O(N2) to O(N). We only concentrated on three states in the United States, one of which was the most affected, one of which was the least affected, and one intermediate state, in order to make our predictions more meaningful. We use mean absolute percentage error and mean absolute error as evaluation indexes. The experimental results show that the proposed time series model has a better predictive performance than the current DL models and traditional models. Additionally, our model's convergence outperforms that of the current DL models, offering a more precise benchmark for the control of epidemics.
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Affiliation(s)
- Yulan Li
- Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China
- Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China
| | - Kun Ma
- Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China
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Estimating the Dissolution of Anticancer Drugs in Supercritical Carbon Dioxide with a Stacked Machine Learning Model. Pharmaceutics 2022; 14:pharmaceutics14081632. [PMID: 36015258 PMCID: PMC9416672 DOI: 10.3390/pharmaceutics14081632] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/25/2022] [Accepted: 07/30/2022] [Indexed: 11/17/2022] Open
Abstract
Synthesizing micro-/nano-sized pharmaceutical compounds with an appropriate size distribution is a method often followed to enhance drug delivery and reduce side effects. Supercritical CO2 (carbon dioxide) is a well-known solvent utilized in the pharmaceutical synthesis process. Reliable knowledge of a drug’s solubility in supercritical CO2 is necessary for feasible study, modeling, design, optimization, and control of such a process. Therefore, the current study constructs a stacked/ensemble model by combining three up-to-date machine learning tools (i.e., extra tree, gradient boosting, and random forest) to predict the solubility of twelve anticancer drugs in supercritical CO2. An experimental databank comprising 311 phase equilibrium samples was gathered from the literature and applied to design the proposed stacked model. This model estimates the solubility of anticancer drugs in supercritical CO2 as a function of solute and solvent properties and operating conditions. Several statistical indices, including average absolute relative deviation (AARD = 8.62%), mean absolute error (MAE = 2.86 × 10−6), relative absolute error (RAE = 2.42%), mean squared error (MSE = 1.26 × 10−10), and regression coefficient (R2 = 0.99809) were used to validate the performance of the constructed model. The statistical, sensitivity, and trend analyses confirmed that the suggested stacked model demonstrates excellent performance for correlating and predicting the solubility of anticancer drugs in supercritical CO2.
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