PMID- 35660327 OWN - NLM STAT- MEDLINE DCOM- 20220713 LR - 20220916 IS - 1879-0534 (Electronic) IS - 0010-4825 (Linking) VI - 147 DP - 2022 Aug TI - HGSORF: Henry Gas Solubility Optimization-based Random Forest for C-Section prediction and XAI-based cause analysis. PG - 105671 LID - S0010-4825(22)00459-0 [pii] LID - 10.1016/j.compbiomed.2022.105671 [doi] AB - A stable predictive model is essential for forecasting the chances of cesarean or C-section (CS) delivery, as unnecessary CS delivery can adversely affect neonatal, maternal, and pediatric morbidity and mortality, and can incur significant financial burdens. Limited state-of-the-art machine learning models have been applied in this area in recent years, and the current models are insufficient to correctly predict the probability of CS delivery. To alleviate this drawback, we have proposed a Henry gas solubility optimization (HGSO)-based random forest (RF), with an improved objective function, called HGSORF, for the classification of CS and non-CS classes. Real-world CS datasets can be noisy, such as the Pakistan Demographic and Health Survey (PDHS) dataset used in this study. The HGSO can provide fine-tuned hyperparameters of RF by avoiding local minima points. To compare performance, Gaussian Naive Bayes (GNB), linear discriminant analysis (LDA), K-nearest neighbors (KNN), gradient boosting classifier (GBC), and logistic regression (LR) have been considered in this research. The ADAptive SYNthetic (ADASYN) algorithm has been used to balance the model, and the proposed HGSORF has been compared with other classifiers as well as with other studies. The superior performance was achieved by HGSORF with an accuracy of 98.33% for the PDHS dataset. The hyperparameters of RF have also been optimized by using commonly used hyperparameter-optimization algorithms, and the proposed HGSORF provided comparatively better performance. Additionally, to analyze the causes of CS and their significance, the HGSORF is explained locally and globally using eXplainable artificial intelligence (XAI)-based tools such as SHapely Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). A decision support system has been developed as a potential application to support clinical staffs. All pre-trained models and relevant codes are available on: https://github.com/MIrazul29/HGSORF_CSection. CI - Copyright (c) 2022 Elsevier Ltd. All rights reserved. FAU - Islam, Md Saiful AU - Islam MS AD - Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia. Electronic address: saislam@ksu.edu.sa. FAU - Awal, Md Abdul AU - Awal MA AD - Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh. Electronic address: m.awal@ece.ku.ac.bd. FAU - Laboni, Jinnaton Nessa AU - Laboni JN AD - Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh. Electronic address: labonihasan456@gmail.com. FAU - Pinki, Farhana Tazmim AU - Pinki FT AD - Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh. Electronic address: farhana@ku.ac.bd. FAU - Karmokar, Shatu AU - Karmokar S AD - Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh. Electronic address: shatunatore98@gmail.com. FAU - Mumenin, Khondoker Mirazul AU - Mumenin KM AD - Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh. Electronic address: k.mirazulmumenin@gmail.com. FAU - Al-Ahmadi, Saad AU - Al-Ahmadi S AD - Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia. Electronic address: salahmadi@ksu.edu.sa. FAU - Rahman, Md Ashfikur AU - Rahman MA AD - Development Studies Discipline, Khulna University, Khulna 9208, Bangladesh. Electronic address: ashfikur@ku.ac.bd. FAU - Hossain, Md Shahadat AU - Hossain MS AD - Department of Quantitative Sciences, International University of Business Agriculture and Technology, Dhaka 1230, Bangladesh. Electronic address: shahadat_qs@iubat.edu. FAU - Mirjalili, Seyedali AU - Mirjalili S AD - Centre for Artificial Intelligence Research and Optimization, Torrens University, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea. Electronic address: ali.mirjalili@torrens.edu.au. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20220530 PL - United States TA - Comput Biol Med JT - Computers in biology and medicine JID - 1250250 SB - IM MH - Algorithms MH - *Artificial Intelligence MH - Bayes Theorem MH - Child MH - Humans MH - Infant, Newborn MH - *Machine Learning MH - Solubility OTO - NOTNLM OT - ADASYN OT - Cesarean section OT - HGSORF OT - Hyperparameter optimization OT - LIME OT - Machine learning OT - SHAP OT - XAI EDAT- 2022/06/07 06:00 MHDA- 2022/07/14 06:00 CRDT- 2022/06/06 11:33 PHST- 2021/11/12 00:00 [received] PHST- 2022/05/24 00:00 [revised] PHST- 2022/05/24 00:00 [accepted] PHST- 2022/06/07 06:00 [pubmed] PHST- 2022/07/14 06:00 [medline] PHST- 2022/06/06 11:33 [entrez] AID - S0010-4825(22)00459-0 [pii] AID - 10.1016/j.compbiomed.2022.105671 [doi] PST - ppublish SO - Comput Biol Med. 2022 Aug;147:105671. doi: 10.1016/j.compbiomed.2022.105671. Epub 2022 May 30.