{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T01:18:19Z","timestamp":1793841499678,"version":"4.6.1"},"reference-count":83,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Polycystic ovary syndrome (PCOS) is a medical condition that impacts millions of women worldwide; however, due to a lack of public awareness, as well as the expensive testing involved in the identification of PCOS, 70% of cases go undiagnosed. Therefore, the primary objective of this study is to design an expert machine learning (ML) model for the early diagnosis of PCOS based on initial symptoms and health indicators; two datasets were amalgamated and preprocessed to accomplish this goal, resulting in a new symptomatic dataset with 12 attributes. An ensemble learning (EL) model, with seven base classifiers, and a deep learning (DL) model, as the meta-level classifier, are proposed. The hyperparameters of the EL model were optimized through the nature-inspired walrus optimization (WaO), cuckoo search optimization (CSO), and random search optimization (RSO) algorithms, leading to the WaOEL, CSOEL, and RSOEL models, respectively. The results obtained prove the supremacy of the designed WaOEL model over the other models, with a PCOS prediction accuracy of 92.8% and an area under the receiver operating characteristic curve (AUC) of 0.93; moreover, feature importance analysis, presented with random forest (RF) and Shapley additive values (SHAP) for positive PCOS predictions, highlights crucial clinical insights and the need for early intervention. Our findings suggest that patients with features related to obesity and high cholesterol are more likely to be diagnosed as PCOS positive. Most importantly, it is inferred from this study that early PCOS identification without expensive tests is possible with the proposed WaOEL, which helps clinicians and patients make better informed decisions, identify comorbidities, and reduce the harmful long-term effects of PCOS.<\/jats:p>","DOI":"10.3390\/s25041166","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T04:48:51Z","timestamp":1739508531000},"page":"1166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Optimized Machine Learning for the Early Detection of Polycystic Ovary Syndrome in Women"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9889-6690","authenticated-orcid":false,"given":"Bharti","family":"Panjwani","sequence":"first","affiliation":[{"name":"Department of Computer Science & Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Bantakal 574115, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6429-7742","authenticated-orcid":false,"given":"Jyoti","family":"Yadav","sequence":"additional","affiliation":[{"name":"Department of Instrumentation and Control Engineering, Netaji Subhas University of Technology, Sector-3 Dwarka, New Delhi 110078, Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1457-2651","authenticated-orcid":false,"given":"Vijay","family":"Mohan","sequence":"additional","affiliation":[{"name":"Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5067-0203","authenticated-orcid":false,"given":"Neha","family":"Agarwal","sequence":"additional","affiliation":[{"name":"School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3836-2595","authenticated-orcid":false,"given":"Saurabh","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Barrera, F.J., Brown, E.D.L., Rojo, A., Obeso, J., Plata, H., Lincango, E.P., Terry, N., Rodriguez-Gutierrez, R., Hall, J.E., and Shekhar, S. (2023). Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: A systematic review. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1106625"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1038\/nrendo.2018.24","article-title":"Polycystic ovary syndrome: Definition, aetiology, diagnosis and treatment","volume":"14","year":"2018","journal-title":"Nat. Rev. Endocrinol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"522","DOI":"10.18231\/j.ijogr.2019.112","article-title":"A cross sectional study on prevalence of PCOS and risk factors associated with it among medical students","volume":"6","author":"Aggarwal","year":"2019","journal-title":"Indian J. Obstet. Gynecol. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sadeghi, H.M., Adeli, I., Calina, D., Docea, A.O., Mousavi, T., Daniali, M., Nikfar, S., Tsatsakis, A., and Abdollahi, M. (2022). Polycystic ovary syndrome: A comprehensive review of pathogenesis, management, and drug repurposing. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23020583"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1097\/AOG.0b013e31820209bb","article-title":"Polycystic ovary syndrome and risk for long-term diabetes and dyslipidemia","volume":"117","author":"Wang","year":"2011","journal-title":"Obstet. Gynecol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100085","DOI":"10.1016\/j.endmts.2021.100085","article-title":"A review: Brief insight into Polycystic Ovarian syndrome","volume":"3","author":"Bulsara","year":"2021","journal-title":"Endocr. Metab. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"261","DOI":"10.4103\/jhrs.JHRS_95_18","article-title":"The prevalence of polycystic ovary syndrome: A brief systematic review","volume":"13","author":"Deswal","year":"2020","journal-title":"J. Hum. Reprod. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.ejogrb.2016.08.026","article-title":"Obesity, type 2 diabetes mellitus and cardiovascular disease risk: An uptodate in the management of polycystic ovary syndrome","volume":"207","author":"Orio","year":"2016","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e85338","DOI":"10.7554\/eLife.85338","article-title":"Direct economic burden of mental health disorders associated with polycystic ovary syndrome: Systematic review and meta-analysis","volume":"12","author":"Yadav","year":"2023","journal-title":"eLife"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Neubronner, S.A., Indran, I.R., Chan, Y.H., Thu, A.W.P., and Yong, E.-L. (2021). Effect of body mass index (BMI) on phenotypic features of polycystic ovary syndrome (PCOS) in Singapore women: A prospective cross-sectional study. BMC Women\u2019s Health, 21.","DOI":"10.1186\/s12905-021-01277-6"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"627","DOI":"10.2337\/db20-0800","article-title":"Polycystic ovary syndrome and risk of type 2 diabetes, coronary heart disease, and stroke","volume":"70","author":"Zhu","year":"2021","journal-title":"Diabetes"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102970","DOI":"10.1016\/j.dsx.2024.102970","article-title":"Diagnostic accuracy of oral glucose tolerance tests, fasting plasma glucose and haemoglobin A1c for type 2 diabetes in women with polycystic ovary syndrome: A systematic review and meta-analysis","volume":"18","author":"Belsti","year":"2024","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Henney, A.E., Gillespie, C.S., Lai, J.Y., Schofield, P., Riley, D.R., Caleyachetty, R., Barber, T.M., Miras, A.D., Dobbie, L.J., and Hughes, D.M. (2024). Risk of type 2 diabetes, MASLD and cardiovascular disease in people living with polycystic ovary syndrome. J. Clin. Endocrinol. Metab., dgae481.","DOI":"10.1210\/clinem\/dgae481"},{"key":"ref_14","first-page":"1","article-title":"The Role of Diabetes on Polycystic Ovary Syndrome (PCOS)","volume":"1","year":"2024","journal-title":"Int. Innov. J. Appl. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1093\/humupd\/dmaa029","article-title":"Long-term cardiometabolic disease risk in women with PCOS: A systematic review and meta-analysis","volume":"26","author":"Wekker","year":"2020","journal-title":"Hum. Reprod. Update"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wal, A., Dash, B., Jaiswal, V., Gupta, D., and Mishra, A.K. (2024). Role of inflammation, oxidative stress, and angiogenesis in polycystic ovary syndrome (PCOS): Current perspectives. Targeting Angiogenesis, Inflammation, and Oxidative Stress in Chronic Diseases, Academic Press.","DOI":"10.1016\/B978-0-443-13587-3.00018-7"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11892-017-0956-2","article-title":"Cardiometabolic risk in PCOS: More than a reproductive disorder","volume":"17","author":"Torchen","year":"2017","journal-title":"Curr. Diabetes Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1007\/s40618-023-02230-0","article-title":"Alterations of cardiometabolic risk profile in polycystic ovary syndrome: 13 years follow-up in an unselected population","volume":"47","author":"Aksun","year":"2024","journal-title":"J. Endocrinol. Investig."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1210\/clinem\/dgab613","article-title":"Health care-related economic burden of polycystic ovary syndrome in the United States: Pregnancy-related and long-term health consequences","volume":"107","author":"Riestenberg","year":"2022","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108197","DOI":"10.1016\/j.pec.2024.108197","article-title":"Healthcare professionals\u2019 perspectives on the challenges with managing polycystic ovary syndrome: A systematic review and meta-synthesis","volume":"123","author":"Guan","year":"2024","journal-title":"Patient Educ. Couns."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rehman, R., Alam, F., and Khan, R. (2024). Situation analysis of polycystic ovary syndrome in Central and East Asia. Polycystic Ovary Syndrome, Elsevier.","DOI":"10.1016\/B978-0-323-87932-3.00034-7"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1056\/NEJM199509283331307","article-title":"Polycystic ovary syndrome","volume":"333","author":"Franks","year":"1995","journal-title":"N. Engl. J. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1056\/NEJMcp1514916","article-title":"Polycystic ovary syndrome","volume":"375","author":"McCartney","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16057","DOI":"10.1038\/nrdp.2016.57","article-title":"Polycystic ovary syndrome","volume":"2","author":"Azziz","year":"2016","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"745","DOI":"10.2147\/IJWH.S70314","article-title":"Complications and challenges associated with polycystic ovary syndrome: Current perspectives","volume":"7","author":"Palomba","year":"2015","journal-title":"Int. J. Women\u2019s Health"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.xfre.2024.05.004","article-title":"Lack of adequate counseling about pregnancy complications in patients with polycystic ovary syndrome: A cross-sectional survey study","volume":"5","author":"Kim","year":"2024","journal-title":"F&S Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"903","DOI":"10.2165\/00003495-200666070-00002","article-title":"Diagnosis and management of polycystic ovary syndrome: A practical guide","volume":"66","author":"Stankiewicz","year":"2006","journal-title":"Drugs"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.bpobgyn.2016.03.009","article-title":"Diagnostic criteria for PCOS: Is there a need for a rethink?","volume":"37","author":"Dewailly","year":"2016","journal-title":"Best Pract. Res. Clin. Obstet. Gynaecol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.jbi.2005.05.005","article-title":"A medical bioinformatics approach for metabolic disorders: Biomedical data prediction, modeling, and systematic analysis","volume":"39","author":"Chen","year":"2006","journal-title":"J. Biomed. Inform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8929","DOI":"10.2147\/CMAR.S341485","article-title":"Bioinformatics analysis of hub genes and potential therapeutic agents associated with gastric cancer","volume":"13","author":"Zhang","year":"2021","journal-title":"Cancer Manag. Res."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, B. (2014). Big Data Analytics in Bioinformatics and Healthcare, IGI Global.","DOI":"10.4018\/978-1-4666-6611-5"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"335","DOI":"10.30574\/wjarr.2024.21.1.0016","article-title":"Bioinformatics and personalized medicine in the US: A comprehensive review: Scrutinizing the advancements in genomics and their potential to revolutionize healthcare delivery","volume":"21","author":"Olorunsogo","year":"2024","journal-title":"World J. Adv. Res. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s12553-023-00805-8","article-title":"Unsupervised machine learning for disease prediction: A comparative performance analysis using multiple datasets","volume":"14","author":"Lu","year":"2024","journal-title":"Health Technol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Park, D.J., Park, M.W., Lee, H., Kim, Y.-J., Kim, Y., and Park, Y.H. (2021). Development of machine learning model for diagnostic disease prediction based on laboratory tests. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-87171-5"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Krittanawong, C., Virk, H.U.H., Bangalore, S., Wang, Z., Johnson, K.W., Pinotti, R., Zhang, H., Kaplin, S., Narasimhan, B., and Kitai, T. (2020). Machine learning prediction in cardiovascular diseases: A meta-analysis. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-72685-1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1007\/s10586-022-03707-y","article-title":"Popular deep learning algorithms for disease prediction: A review","volume":"26","author":"Yu","year":"2023","journal-title":"Clust. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.inffus.2020.06.008","article-title":"A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion","volume":"63","author":"Ali","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.artmed.2019.07.007","article-title":"Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes","volume":"98","author":"Woldaregay","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Y.-Q., Chang, T.-W., Lee, L.-C., Chen, C.-Y., Hsu, P.-S., Tsan, Y.-T., Yang, C.-T., and Chu, W.-M. (2024). Use of Machine Learning to Predict the Incidence of Type 2 Diabetes Among Relatively Healthy Adults: A 10-Year Longitudinal Study in Taiwan. Diagnostics, 15.","DOI":"10.3390\/diagnostics15010072"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ahmad, R., Maghrabi, L.A., Khaja, I.A., Maghrabi, L.A., and Ahmad, M. (2024). SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models. Diagnostics, 14.","DOI":"10.3390\/diagnostics14192225"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"11142","DOI":"10.1002\/int.23036","article-title":"A differential privacy-preserving deep learning caching framework for heterogeneous communication network systems","volume":"37","author":"Wang","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"117592","DOI":"10.1016\/j.eswa.2022.117592","article-title":"SPOSDS: A smart Polycystic Ovary Syndrome diagnostic system using machine learning","volume":"203","author":"Tiwari","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Elmannai, H., El-Rashidy, N., Mashal, I., Alohali, M.A., Farag, S., El-Sappagh, S., and Saleh, H. (2023). Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence. Diagnostics, 13.","DOI":"10.3390\/diagnostics13081506"},{"key":"ref_44","unstructured":"Prajna, K.B., Iyer, B.V., Bhuvan, C., Thambanda, K.M., and Kanasu, H.R. (2023, January 26\u201328). Implementation of Various Machine Learning Algorithms to Predict Polycystic Ovary Syndrome. Proceedings of the 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Khanna, V.V., Chadaga, K., Sampathila, N., Prabhu, S., Bhandage, V., and Hegde, G.K. (2023). A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome. Appl. Syst. Innov., 6.","DOI":"10.3390\/asi6020032"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"97610","DOI":"10.1109\/ACCESS.2022.3205587","article-title":"A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics","volume":"10","author":"Nasim","year":"2022","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"240","DOI":"10.21786\/bbrc\/13.14\/56","article-title":"PCOcare: PCOS Detection and Prediction using Machine Learning Algorithms","volume":"13","author":"Thakre","year":"2020","journal-title":"Biosci. Biotechnol. Res. Commun."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"86522","DOI":"10.1109\/ACCESS.2023.3304536","article-title":"A Review on the Detection Techniques of Polycystic Ovary Syndrome Using Machine Learning","volume":"11","author":"Ahmed","year":"2023","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Prabha, A., Yadav, J., Rani, A., and Singh, V. (2022). Intelligent estimation of blood glucose level using wristband PPG signal and physiological parameters. Biomed. Signal Process. Control, 78.","DOI":"10.1016\/j.bspc.2022.103876"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e35283","DOI":"10.1016\/j.heliyon.2024.e35283","article-title":"Predicting TCM patterns in PCOS patients: An exploration of feature selection methods and multi-label machine learning models","volume":"10","author":"Lim","year":"2024","journal-title":"Heliyon"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e358","DOI":"10.1016\/j.fertnstert.2024.08.113","article-title":"Prediction of polycystic ovary syndrome (pcos) using self-reported characteristics from a digital cohort in the unites states","volume":"122","author":"Wang","year":"2024","journal-title":"Fertil. Steril."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"108298","DOI":"10.1016\/j.compeleceng.2022.108298","article-title":"Food Image-based diet recommendation framework to overcome PCOS problem in women using deep convolutional neural network","volume":"103","author":"Kaur","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Khushal, R., and Fatima, U. (2024). Fuzzy machine learning logic utilization on hormonal imbalance dataset. Comput. Biol. Med., 174.","DOI":"10.1016\/j.compbiomed.2024.108429"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"101500","DOI":"10.1016\/j.imu.2024.101500","article-title":"Empowering early detection: A web-based machine learning approach for PCOS prediction","volume":"47","author":"Rahman","year":"2024","journal-title":"Inform. Med. Unlocked"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e29967","DOI":"10.2196\/29967","article-title":"Machine-aided self-diagnostic prediction models for polycystic ovary syndrome: Observational study","volume":"6","author":"Zigarelli","year":"2022","journal-title":"JMIR Form. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"119532","DOI":"10.1016\/j.eswa.2023.119532","article-title":"Early identification of PCOS with commonly known diseases: Obesity, diabetes, high blood pressure and heart disease using machine learning techniques","volume":"217","author":"Aggarwal","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1093\/humupd\/dmy007","article-title":"Ethnicity, obesity and the prevalence of impaired glucose tolerance and type 2 diabetes in PCOS: A systematic review and meta-regression","volume":"24","author":"Kakoly","year":"2018","journal-title":"Hum. Reprod. Update"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1007\/s12273-022-0908-x","article-title":"Regression tree ensemble learning-based prediction of the heating and cooling loads of residential buildings","volume":"15","author":"Pachauri","year":"2022","journal-title":"Build. Simul."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"108455","DOI":"10.1016\/j.jobe.2024.108455","article-title":"Investigation of optimized machine learning models with PSO for forecasting the shear capacity of steel fiber-reinforced SCC beams with\/out stirrups","volume":"83","author":"Ergen","year":"2024","journal-title":"J. Build. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, X., Lu, B., Zhang, L., Pan, Z., Liao, M., Shen, H., Zhang, L., Liu, L., Li, Z., and Hu, Y. (2023). An enhanced grey wolf optimizer boosted machine learning prediction model for patient-flow prediction. Comput. Biol. Med., 163.","DOI":"10.1016\/j.compbiomed.2023.107166"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"111192","DOI":"10.1016\/j.asoc.2023.111192","article-title":"A genetic operators-based Ant Lion Optimiser for training a medical multi-layer perceptron","volume":"151","author":"Rojas","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"111850","DOI":"10.1016\/j.knosys.2024.111850","article-title":"Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm","volume":"295","author":"Ghasemi","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Saman, S., and Narayanan, S.J. (2024). Optimal feature subset selection for MRI brain tumor classification using improved ant-lion optimization. Evolutionary Intelligence, Springer.","DOI":"10.1007\/s12065-024-00925-8"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1007\/s11042-022-13437-3","article-title":"Computer vision model with novel cuckoo search based deep learning approach for classification of fish image","volume":"82","author":"Aziz","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_65","unstructured":"Lundberg, S. (2017). A unified approach to interpreting model predictions. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ejogrb.2024.02.011","article-title":"Distribution and anthropometric characteristics of Rotterdam criteria-based phenotypic forms of Polycystic ovaries syndrome in Ukraine","volume":"295","author":"Tatarchuk","year":"2024","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_67","unstructured":"Heart Disease Dataset (2024, June 10). Kaggle 2019. Available online: https:\/\/www.kaggle.com\/datasets\/johnsmith88\/heart-disease-dataset."},{"key":"ref_68","unstructured":"Polycystic Ovary Syndrome (PCOS) (2024, June 10). Kaggle 2020. Available online: https:\/\/www.kaggle.com\/datasets\/prasoonkottarathil\/polycystic-ovary-syndrome-pcos."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Uddin, S., Haque, I., Lu, H., Moni, M.A., and Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-10358-x"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Palanisamy, S., and Rajaguru, H. (2024). Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals. Diagnostics, 14.","DOI":"10.20944\/preprints202407.1991.v1"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Thomas, N.M., and Jerome, S.A. (2024). Diabetic retinopathy detection using ensembled transfer learning based thrice CNN with SVM classifier. Multimedia Tools and Applications, Springer.","DOI":"10.1007\/s11042-024-18403-9"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1007\/s11277-024-10961-3","article-title":"A New Discrete Learning-Based Logistic Regression Classifier for Bankruptcy Prediction","volume":"134","author":"Khashei","year":"2024","journal-title":"Wirel. Pers. Commun."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Ray, A., and Chaudhuri, A.K. (2024). A Novel Diagnosis System for Parkinson\u2019s Disease Based on Ensemble Random Forest. Data Driven Science for Clinically Actionable Knowledge in Diseases, Chapman and Hall\/CRC.","DOI":"10.1201\/9781003292357-4"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1007\/s10916-016-0467-8","article-title":"Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier","volume":"40","author":"Alickovic","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Alghazzawi, D.M., Alquraishee, A.G.A., Badri, S.K., and Hasan, S.H. (2023). ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review. Sustainability, 15.","DOI":"10.3390\/su15097076"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Asif, S., Wenhui, Y., Tao, Y., Jinhai, S., and Jin, H. (2021, January 28\u201331). An Ensemble Machine Learning Method for the Prediction of Heart Disease. Proceedings of the 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China.","DOI":"10.1109\/ICAIBD51990.2021.9459010"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Isabona, J., Imoize, A.L., and Kim, Y. (2022). Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning. Sensors, 22.","DOI":"10.3390\/s22103776"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"119085","DOI":"10.1016\/j.powtec.2023.119085","article-title":"Study on characteristics and parameter optimization of medical waste crushing process","volume":"431","author":"Liu","year":"2024","journal-title":"Powder Technol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"8775","DOI":"10.1038\/s41598-023-35863-5","article-title":"A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior","volume":"13","author":"Trojovsky","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"122413","DOI":"10.1016\/j.eswa.2023.122413","article-title":"Walrus optimizer: A novel nature-inspired metaheuristic algorithm","volume":"239","author":"Han","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11831-023-09975-0","article-title":"A literature review and critical analysis of metaheuristics recently developed","volume":"31","author":"Velasco","year":"2024","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1038\/sj.ijo.0801994","article-title":"Obesity and the polycystic ovary syndrome","volume":"26","author":"Gambineri","year":"2002","journal-title":"Int. J. Obes."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Daescu, A.-M.C., Dehelean, L., Navolan, D.-B., Gaitoane, A.-I., Daescu, A., and Stoian, D. (2023). Effects of hormonal profile, weight, and body image on sexual function in women with polycystic ovary syndrome. 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