{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:32:44Z","timestamp":1790620364838,"version":"4.1.0"},"reference-count":101,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Ministry of Education (MOE) and Gangwon State","award":["2025-RISE-10-00.1"],"award-info":[{"award-number":["2025-RISE-10-00.1"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including tremor, rigidity, and postural instability. Machine learning (ML) models have shown promise for the diagnosis of PD; however, many existing approaches do not explicitly address fairness and robustness. As a result, these models can lead to biased outcomes across demographic groups and vulnerability to adversarial attacks. In this study, we used the Parkinson\u2019s Progression Markers Initiative (PPMI) cohort, which includes clinical and demographic information from 1,084 participants spanning diverse age, sex, and racial groups. Our study addresses the key challenge of developing robust and equitable ML models to diagnose the progression of PD. We evaluated the performance of two fairness-optimized classifiers, namely, Random Forest (RF) and Decision Tree (DT). To evaluate model vulnerability, we applied adversarial techniques, specifically label leakage and data poisoning attacks, which simulate intentional or erroneous data alterations that can amplify biases and degrade accuracy. These adversarial manipulations substantially degraded model performance; specifically, DT accuracy declined by more than 10% between sensitive groups. The accuracy of the RF model decreased by 20%. Moreover, under attack, fairness metrics such as Statistical Parity Difference (SPD), which looks at differences in the chances of getting a positive prediction across demographic groups, and Equal Opportunity Difference (EOD) for differences in true positive rates between groups, both showed a decline. This pattern suggests that adversarial perturbations increased bias and widened performance disparities across demographic groups. Our results demonstrated that adversarial attacks increased the incidence of false positives and false negatives, thereby lowering the accuracy and fairness of the PD diagnostic predictions. These findings underscore the urgent need for robust and fairness-aware defenses in medical AI to mitigate racial, age, and gender disparities and ensure a reliable clinical decision-making process.<\/jats:p>","DOI":"10.1371\/journal.pone.0342062","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T18:49:39Z","timestamp":1771872579000},"page":"e0342062","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Trustworthy AI for medical decisions: Adversarially robust and fair machine learning prediction for Parkinson\u2019s disease"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3461-6773","authenticated-orcid":true,"given":"Junaid","family":"Muhammad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5652-4960","authenticated-orcid":true,"given":"Mitra","family":"Ghergherehchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiraz","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5913-3364","authenticated-orcid":true,"given":"Ho","family":"Seung Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6274-2593","authenticated-orcid":true,"given":"Nasir","family":"Rahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"key":"pone.0342062.ref001","unstructured":"Tsipras D, Santurkar S, Engstrom L, Turner A, Madry A. Robustness may be at odds with accuracy. arXiv preprint 2018. https:\/\/arxiv.org\/abs\/1805.12152"},{"key":"pone.0342062.ref002","doi-asserted-by":"crossref","unstructured":"Carlini N, Wagner D. Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP). 2017. p. 39\u201357. https:\/\/doi.org\/10.1109\/sp.2017.49","DOI":"10.1109\/SP.2017.49"},{"key":"pone.0342062.ref003","unstructured":"Turner A, Tsipras D, Madry A. Label-consistent backdoor attacks. arXiv preprint 2019. https:\/\/arxiv.org\/abs\/1912.02771"},{"issue":"7","key":"pone.0342062.ref004","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1145\/3134599","article-title":"Making machine learning robust against adversarial inputs","volume":"61","author":"I Goodfellow","year":"2018","journal-title":"Commun ACM."},{"key":"pone.0342062.ref005","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli SM, Fawzi A, Frossard P. Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 2574\u201382.","DOI":"10.1109\/CVPR.2016.282"},{"key":"pone.0342062.ref006","doi-asserted-by":"crossref","unstructured":"Papernot N, McDaniel P, Jha S, Fredrikson M, Celik ZB, Swami A. The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P). 2016. p. 372\u201387. https:\/\/doi.org\/10.1109\/eurosp.2016.36","DOI":"10.1109\/EuroSP.2016.36"},{"key":"pone.0342062.ref007","first-page":"289","article-title":"A causal view on robustness of neural networks","volume":"33","author":"C Zhang","year":"2020","journal-title":"Advances in Neural Information Processing Systems."},{"key":"pone.0342062.ref008","doi-asserted-by":"crossref","first-page":"108348","DOI":"10.1016\/j.cmpb.2024.108348","article-title":"Alzheimer\u2019s disease diagnosis in the metaverse","volume":"255","author":"JS Bazargani","year":"2024","journal-title":"Comput Methods Programs Biomed."},{"issue":"4","key":"pone.0342062.ref009","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/MIS.2020.3000681","article-title":"Fairness in deep learning: a computational perspective","volume":"36","author":"M Du","year":"2021","journal-title":"IEEE Intell Syst."},{"key":"pone.0342062.ref010","doi-asserted-by":"crossref","first-page":"105491","DOI":"10.1016\/j.imavis.2025.105491","article-title":"Early progression detection from MCI to AD using multi-view MRI for enhanced assisted living","volume":"157","author":"N Rahim","year":"2025","journal-title":"Image and Vision Computing."},{"issue":"4","key":"pone.0342062.ref011","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1007\/s10551-021-04939-5","article-title":"Do the ends justify the means? Variation in the distributive and procedural fairness of machine learning algorithms","volume":"181","author":"L Morse","year":"2021","journal-title":"J Bus Ethics."},{"issue":"3","key":"pone.0342062.ref012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3494672","article-title":"A review on fairness in machine learning","volume":"55","author":"D Pessach","year":"2022","journal-title":"ACM Comput Surv."},{"issue":"6","key":"pone.0342062.ref013","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3457607","article-title":"A survey on bias and fairness in machine learning","volume":"54","author":"N Mehrabi","year":"2021","journal-title":"ACM Comput Surv."},{"issue":"10","key":"pone.0342062.ref014","doi-asserted-by":"crossref","first-page":"7643","DOI":"10.1109\/JBHI.2025.3569891","article-title":"An interpretable AI for smart homes: identifying fall prevention strategies for older adults using multimodal deep learning","volume":"29","author":"J Baek","year":"2025","journal-title":"IEEE J Biomed Health Inform."},{"issue":"9","key":"pone.0342062.ref015","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1038\/s41591-019-0548-6","article-title":"Do no harm: a roadmap for responsible machine learning for health care","volume":"25","author":"J Wiens","year":"2019","journal-title":"Nat Med."},{"key":"pone.0342062.ref016","doi-asserted-by":"crossref","unstructured":"Wang D, Wang L, Zhang Z, Wang D, Zhu H, Gao Y, et al. \u201cBrilliant AI Doctor\u201d in rural clinics: challenges in AI-powered clinical decision support system deployment. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 2021. p. 1\u201318. https:\/\/doi.org\/10.1145\/3411764.3445432","DOI":"10.1145\/3411764.3445432"},{"key":"pone.0342062.ref017","doi-asserted-by":"crossref","unstructured":"Toreini E, Aitken M, Coopamootoo K, Elliott K, Zelaya CG, van Moorsel A. The relationship between trust in AI and trustworthy machine learning technologies. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020. p. 272\u201383. https:\/\/doi.org\/10.1145\/3351095.3372834","DOI":"10.1145\/3351095.3372834"},{"key":"pone.0342062.ref018","doi-asserted-by":"crossref","first-page":"4900511","DOI":"10.1109\/JTEHM.2021.3073629","article-title":"Clinically applicable machine learning approaches to identify attributes of Chronic Kidney Disease (CKD) for use in low-cost diagnostic screening","volume":"9","author":"M Rashed-Al-Mahfuz","year":"2021","journal-title":"IEEE J Transl Eng Health Med."},{"issue":"9","key":"pone.0342062.ref019","doi-asserted-by":"crossref","first-page":"6092","DOI":"10.1109\/TII.2020.2974555","article-title":"A trustworthy privacy preserving framework for machine learning in industrial IoT systems","volume":"16","author":"PCM Arachchige","year":"2020","journal-title":"IEEE Trans Ind Inf."},{"key":"pone.0342062.ref020","doi-asserted-by":"crossref","first-page":"100270","DOI":"10.1016\/j.cosrev.2020.100270","article-title":"A survey of safety and trustworthiness of deep neural networks: verification, testing, adversarial attack and defence, and interpretability","volume":"37","author":"X Huang","year":"2020","journal-title":"Computer Science Review."},{"key":"pone.0342062.ref021","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jpdc.2020.07.003","article-title":"Knowledge-driven machine learning based framework for early-stage disease risk prediction in edge environment","volume":"146","author":"MA Hossain","year":"2020","journal-title":"Journal of Parallel and Distributed Computing."},{"issue":"10","key":"pone.0342062.ref022","doi-asserted-by":"crossref","first-page":"7643","DOI":"10.1109\/JBHI.2025.3569891","article-title":"An interpretable AI for smart homes: identifying fall prevention strategies for older adults using multimodal deep learning","volume":"29","author":"J Baek","year":"2025","journal-title":"IEEE J Biomed Health Inform."},{"issue":"21","key":"pone.0342062.ref023","doi-asserted-by":"crossref","first-page":"15965","DOI":"10.1109\/JIOT.2021.3055804","article-title":"Trustworthy and intelligent COVID-19 diagnostic IoMT through XR and deep-learning-based clinic data access","volume":"8","author":"Y Tai","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"pone.0342062.ref024","doi-asserted-by":"crossref","DOI":"10.1147\/JRD.2019.2942288","article-title":"FactSheets: increasing trust in AI services through supplier\u2019s declarations of conformity","volume":"63","author":"M Arnold","year":"2019","journal-title":"IBM J Res & Dev."},{"key":"pone.0342062.ref025","article-title":"Optimized pre-processing for discrimination prevention","volume":"30","author":"F Calmon","year":"2017","journal-title":"Advances in neural information processing systems."},{"issue":"3","key":"pone.0342062.ref026","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11704-016-6903-6","article-title":"Lifelong machine learning: a paradigm for continuous learning","volume":"11","author":"B Liu","year":"2017","journal-title":"Front Comput Sci."},{"key":"pone.0342062.ref027","unstructured":"Corbett-Davies S, Goel S. The measure and mismeasure of fairness: a critical review of fair machine learning. arXiv preprint 2018. https:\/\/arxiv.org\/abs\/1808.00023"},{"key":"pone.0342062.ref028","article-title":"Fairness through computationally-bounded awareness","volume":"31","author":"M Kim","year":"2018","journal-title":"Advances in Neural Information Processing Systems."},{"issue":"3","key":"pone.0342062.ref029","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0319861","article-title":"Public perception of accuracy-fairness trade-offs in algorithmic decisions in the United States","volume":"20","author":"M Mourali","year":"2025","journal-title":"PLoS One."},{"key":"pone.0342062.ref030","doi-asserted-by":"crossref","first-page":"128098","DOI":"10.1016\/j.eswa.2025.128098","article-title":"Deep learning methods for autonomous driving scene understanding tasks: a review","volume":"287","author":"M Awan","year":"2025","journal-title":"Expert Systems with Applications."},{"issue":"8","key":"pone.0342062.ref031","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1136\/jnnp.2006.103788","article-title":"Gender differences in Parkinson\u2019s disease","volume":"78","author":"CA Haaxma","year":"2007","journal-title":"J Neurol Neurosurg Psychiatry."},{"issue":"1","key":"pone.0342062.ref032","doi-asserted-by":"crossref","first-page":"166","DOI":"10.3390\/diagnostics12010166","article-title":"Bias investigation in artificial intelligence systems for early detection of Parkinson\u2019s disease: a narrative review","volume":"12","author":"S Paul","year":"2022","journal-title":"Diagnostics (Basel)."},{"issue":"2","key":"pone.0342062.ref033","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1007\/s44230-024-00069-z","article-title":"Single and multi-modal analysis for Parkinson\u2019s disease to detect its underlying factors","volume":"4","author":"N Islam","year":"2024","journal-title":"Hum-Cent Intell Syst."},{"key":"pone.0342062.ref034","doi-asserted-by":"crossref","unstructured":"Rohit SA, Yaswanthram P, Nair PR, Rajendra Prasath S, Akella SV. Prediction of Parkinson\u2019s disease using machine learning models\u2014a classifier analysis. Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2021. Springer; 2021. p. 453\u201360.","DOI":"10.1007\/978-981-16-2164-2_35"},{"issue":"3","key":"pone.0342062.ref035","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.eij.2018.03.002","article-title":"Feature selection and classification systems for chronic disease prediction: a review","volume":"19","author":"D Jain","year":"2018","journal-title":"Egyptian Informatics Journal."},{"key":"pone.0342062.ref036","doi-asserted-by":"crossref","first-page":"117077","DOI":"10.1016\/j.jns.2020.117077","article-title":"An SBM-based machine learning model for identifying mild cognitive impairment in patients with Parkinson\u2019s disease","volume":"418","author":"J Zhang","year":"2020","journal-title":"J Neurol Sci."},{"key":"pone.0342062.ref037","article-title":"Equality of opportunity in classification: a causal approach","volume":"31","author":"J Zhang","year":"2018","journal-title":"Advances in Neural Information Processing Systems."},{"issue":"7","key":"pone.0342062.ref038","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0326080","article-title":"Driven early detection of chronic kidney cancer disease based on machine learning technique","volume":"20","author":"W Almukadi","year":"2025","journal-title":"PLoS One."},{"issue":"11","key":"pone.0342062.ref039","doi-asserted-by":"crossref","first-page":"809","DOI":"10.3390\/brainsci10110809","article-title":"Machine learning\u2019s application in deep brain stimulation for Parkinson\u2019s disease: a review","volume":"10","author":"J Watts","year":"2020","journal-title":"Brain Sci."},{"issue":"3","key":"pone.0342062.ref040","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/3446776","article-title":"Understanding deep learning (still) requires rethinking generalization","volume":"64","author":"C Zhang","year":"2021","journal-title":"Commun ACM."},{"issue":"8","key":"pone.0342062.ref041","doi-asserted-by":"crossref","first-page":"5603","DOI":"10.1007\/s00521-022-07256-8","article-title":"Diagnostic classification of Parkinson\u2019s disease based on non-motor manifestations and machine learning strategies","volume":"35","author":"M Martinez-Eguiluz","year":"2022","journal-title":"Neural Comput & Applic."},{"key":"pone.0342062.ref042","doi-asserted-by":"crossref","first-page":"99382","DOI":"10.1109\/ACCESS.2024.3423323","article-title":"A systematic review of adversarial machine learning attacks, defensive controls, and technologies","volume":"12","author":"J Malik","year":"2024","journal-title":"IEEE Access."},{"key":"pone.0342062.ref043","doi-asserted-by":"crossref","unstructured":"Teodorescu MHM, Yao X. Machine learning fairness is computationally difficult and algorithmically unsatisfactorily solved. In: 2021 IEEE High Performance Extreme Computing Conference (HPEC). 2021. p. 1\u20138. https:\/\/doi.org\/10.1109\/hpec49654.2021.9622861","DOI":"10.1109\/HPEC49654.2021.9622861"},{"key":"pone.0342062.ref044","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"NV Chawla","year":"2002","journal-title":"JAIR."},{"key":"pone.0342062.ref045","unstructured":"Han J, Kamber M, Mining D. Concepts and techniques. Morgan Kaufmann; 2006."},{"issue":"17","key":"pone.0342062.ref046","first-page":"1","article-title":"Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning","volume":"18","author":"A Lema","year":"2017","journal-title":"Journal of Machine Learning Research."},{"issue":"4","key":"pone.0342062.ref047","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1007\/s13311-020-00960-0","article-title":"Overcoming barriers to parkinson disease trial participation: increasing diversity and novel designs for recruitment and retention","volume":"17","author":"PA Vaswani","year":"2020","journal-title":"Neurotherapeutics."},{"issue":"3","key":"pone.0342062.ref048","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s10115-012-0487-8","article-title":"A review of feature selection methods on synthetic data","volume":"34","author":"V Bol\u00f3n-Canedo","year":"2012","journal-title":"Knowl Inf Syst."},{"issue":"4","key":"pone.0342062.ref049","doi-asserted-by":"crossref","first-page":"3184","DOI":"10.1109\/LRA.2019.2925731","article-title":"Deep visual MPC-policy learning for navigation","volume":"4","author":"N Hirose","year":"2019","journal-title":"IEEE Robot Autom Lett."},{"issue":"5","key":"pone.0342062.ref050","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1212\/WNL.17.5.427","article-title":"Parkinsonism: onset, progression and mortality","volume":"17","author":"MM Hoehn","year":"1967","journal-title":"Neurology."},{"key":"pone.0342062.ref051","article-title":"Counterfactual fairness","volume":"30","author":"MJ Kusner","year":"2017","journal-title":"Advances in neural information processing systems."},{"key":"pone.0342062.ref052","doi-asserted-by":"crossref","unstructured":"Yang K, Stoyanovich J. Measuring fairness in ranked outputs. In: Proceedings of the 29th International Conference on Scientific and Statistical Database Management, 2017. p. 1\u20136. https:\/\/doi.org\/10.1145\/3085504.3085526","DOI":"10.1145\/3085504.3085526"},{"key":"pone.0342062.ref053","unstructured":"Kleinberg J, Raghavan M. Selection problems in the presence of implicit bias. arXiv preprint. 2018. https:\/\/arxiv.org\/abs\/1801.03533"},{"key":"pone.0342062.ref054","unstructured":"Kearns M, Roth A, Wu ZS. Meritocratic fairness for cross-population selection. In: International conference on machine learning. 2017. p. 1828\u201336."},{"key":"pone.0342062.ref055","unstructured":"Joseph M, Kearns M, Morgenstern J, Neel S, Roth A. Fair algorithms for infinite and contextual bandits. arXiv preprint 2016. https:\/\/arxiv.org\/abs\/1610.09559"},{"key":"pone.0342062.ref056","unstructured":"Liu Y, Radanovic G, Dimitrakakis C, Mandal D, Parkes DC. Calibrated fairness in bandits. arXiv preprint 2017. https:\/\/arxiv.org\/abs\/1707.01875"},{"key":"pone.0342062.ref057","article-title":"Predict responsibly: improving fairness and accuracy by learning to defer","volume":"31","author":"D Madras","year":"2018","journal-title":"Advances in Neural Information Processing Systems."},{"key":"pone.0342062.ref058","doi-asserted-by":"crossref","unstructured":"Doroudi S, Thomas PS, Brunskill E. Importance sampling for fair policy selection. 2017.","DOI":"10.24963\/ijcai.2018\/729"},{"issue":"1","key":"pone.0342062.ref059","doi-asserted-by":"crossref","first-page":"198","DOI":"10.3390\/ijerph18010198","article-title":"Gender differences and quality of life in Parkinson\u2019s disease","volume":"18","author":"P Crispino","year":"2020","journal-title":"Int J Environ Res Public Health."},{"issue":"6","key":"pone.0342062.ref060","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1159\/000479111","article-title":"Gender differences of nonmotor symptoms affecting quality of life in Parkinson disease","volume":"17","author":"J-E Yoon","year":"2017","journal-title":"Neurodegener Dis."},{"issue":"6","key":"pone.0342062.ref061","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1002\/mds.27349","article-title":"Age- and sex-related heterogeneity in prodromal Parkinson\u2019s disease","volume":"33","author":"S Heinzel","year":"2018","journal-title":"Mov Disord."},{"issue":"6","key":"pone.0342062.ref062","first-page":"36","article-title":"Gender differences in neurotoxicity of the nigrostriatal dopaminergic system: implications for Parkinson\u2019s disease","volume":"3","author":"DE Dluzen","year":"2000","journal-title":"J Gend Specif Med."},{"key":"pone.0342062.ref063","article-title":"Aging, Parkinson\u2019s disease, and models: what are the challenges?","volume":"1","author":"E Rocha","year":"2023","journal-title":"Aging Biol."},{"key":"pone.0342062.ref064","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A. Towards deep learning models resistant to adversarial attacks. arXiv preprint 2017. https:\/\/arxiv.org\/abs\/1706.06083"},{"key":"pone.0342062.ref065","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TSP.2022.3153135","article-title":"Robust aggregation for federated learning","volume":"70","author":"K Pillutla","year":"2022","journal-title":"IEEE Trans Signal Process."},{"issue":"12","key":"pone.0342062.ref066","doi-asserted-by":"crossref","first-page":"9387","DOI":"10.1109\/TPAMI.2024.3418862","article-title":"Federated learning for generalization, robustness, fairness: a survey and benchmark","volume":"46","author":"W Huang","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"pone.0342062.ref067","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.jclinepi.2020.12.018","article-title":"Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably","volume":"133","author":"TE Cowling","year":"2021","journal-title":"J Clin Epidemiol."},{"key":"pone.0342062.ref068","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"SM Lundberg","year":"2017","journal-title":"Advances in Neural Information Processing Systems."},{"issue":"11","key":"pone.0342062.ref069","article-title":"The false hope of current approaches to explainable artificial intelligence in health care","volume":"3","author":"M Ghassemi","year":"2021","journal-title":"Lancet Digit Health."},{"key":"pone.0342062.ref070","doi-asserted-by":"crossref","unstructured":"Papernot N, McDaniel P, Sinha A, Wellman MP. SoK: security and privacy in machine learning. In: 2018 IEEE European Symposium on Security and Privacy (EuroS&P). 2018. p. 399\u2013414. https:\/\/doi.org\/10.1109\/eurosp.2018.00035","DOI":"10.1109\/EuroSP.2018.00035"},{"issue":"4","key":"pone.0342062.ref071","article-title":"A hybrid CNN-LSTM deep learning model for classification of the Parkinson disease","volume":"53","author":"RS El-Sayed","year":"2023","journal-title":"IAENG International Journal of Applied Mathematics."},{"key":"pone.0342062.ref072","doi-asserted-by":"crossref","unstructured":"Azhari M, Alaoui A, Abarda A, Ettaki B, Zerouaoui J. A comparison of random forest methods for solving the problem of pulsar search. In: The Proceedings of the Third International Conference on Smart City Applications. 2019. p. 796\u2013807.","DOI":"10.1007\/978-3-030-37629-1_57"},{"key":"pone.0342062.ref073","doi-asserted-by":"crossref","unstructured":"Ramakrishna S, Sajja V, Jhansi Lakshmi P, Bhupal Naik D, Kalluri HK. Student performance monitoring system using decision tree classifier. Machine intelligence and soft computing: Proceedings of ICMISC 2020. Springer; 2021. p. 393\u2013407.","DOI":"10.1007\/978-981-15-9516-5_33"},{"key":"pone.0342062.ref074","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.procs.2020.03.062","article-title":"Detection of pulsar candidates using bagging method","volume":"170","author":"M Azhari","year":"2020","journal-title":"Procedia Computer Science."},{"key":"pone.0342062.ref075","doi-asserted-by":"crossref","first-page":"589","DOI":"10.3389\/fneur.2018.00589","article-title":"Impact of incident Parkinson\u2019s disease on satisfaction with life","volume":"9","author":"EW Buczak-Stec","year":"2018","journal-title":"Front Neurol."},{"key":"pone.0342062.ref076","unstructured":"Baracas S, Hardt M, Narayanan A. Fairness and machine learning. Recommender systems handbook. 2020. p. 453\u20139."},{"key":"pone.0342062.ref077","doi-asserted-by":"crossref","unstructured":"Mehta S, Bhardwaj R. Deep learning meets traditional machine learning: CNN-SVM hybrid models for Parkinson\u2019s diagnosis. In: 2025 International Conference on Automation and Computation (AUTOCOM). 2025. p. 1402\u20136.","DOI":"10.1109\/AUTOCOM64127.2025.10956635"},{"key":"pone.0342062.ref078","unstructured":"Chang JY, Im EG. Data poisoning attack on random forest classification model. In: Proc of SMA. 2020."},{"issue":"6","key":"pone.0342062.ref079","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/s42256-023-00661-1","article-title":"Towards quantum enhanced adversarial robustness in machine learning","volume":"5","author":"MT West","year":"2023","journal-title":"Nat Mach Intell."},{"key":"pone.0342062.ref080","doi-asserted-by":"crossref","unstructured":"Kanca E, Ayas S, Kablan EB, Ekinci M. Implementation of fast gradient sign adversarial attack on vision transformer model and development of defense mechanism in classification of dermoscopy images. In: 2023 31st Signal Processing and Communications Applications Conference (SIU). 2023. p. 1\u20134. https:\/\/doi.org\/10.1109\/siu59756.2023.10223851","DOI":"10.1109\/SIU59756.2023.10223851"},{"key":"pone.0342062.ref081","unstructured":"Kleinberg J, Mullainathan S, Raghavan M. Inherent trade-offs in the fair determination of risk scores. arXiv preprint 2016. https:\/\/arxiv.org\/abs\/1609.05807"},{"issue":"01","key":"pone.0342062.ref082","doi-asserted-by":"crossref","first-page":"20","DOI":"10.38094\/jastt20165","article-title":"Classification based on decision tree algorithm for machine learning","volume":"2","author":"B Charbuty","year":"2021","journal-title":"JASTT."},{"key":"pone.0342062.ref083","doi-asserted-by":"crossref","unstructured":"Li L, Shi Z, Hu X, Dong B, Qin Y, Liu X, et al. T2isafety: benchmark for assessing fairness, toxicity, and privacy in image generation. In: Proceedings of the Computer Vision and Pattern Recognition Conference. 2025. p. 13381\u201392.","DOI":"10.1109\/CVPR52734.2025.01249"},{"issue":"22","key":"pone.0342062.ref084","doi-asserted-by":"crossref","first-page":"8137","DOI":"10.3390\/app10228137","article-title":"Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier","volume":"10","author":"S Mishra","year":"2020","journal-title":"Applied Sciences."},{"issue":"9","key":"pone.0342062.ref085","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.3390\/sym12091424","article-title":"Feature selection methods simultaneously improve the detection accuracy and model building time of machine learning classifiers","volume":"12","author":"S Alabdulwahab","year":"2020","journal-title":"Symmetry."},{"issue":"4","key":"pone.0342062.ref086","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1109\/TCNS.2022.3145285","article-title":"Robustness verification for machine-learning-based power system dynamic security assessment models under adversarial examples","volume":"9","author":"C Ren","year":"2022","journal-title":"IEEE Trans Control Netw Syst."},{"issue":"1","key":"pone.0342062.ref087","doi-asserted-by":"crossref","DOI":"10.1136\/bmjhci-2021-100457","article-title":"Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction","volume":"29","author":"I Straw","year":"2022","journal-title":"BMJ Health Care Inform."},{"issue":"1","key":"pone.0342062.ref088","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0049124118782533","article-title":"Fairness in criminal justice risk assessments: the state of the art","volume":"50","author":"R Berk","year":"2018","journal-title":"Sociological Methods & Research."},{"key":"pone.0342062.ref089","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1186\/s12911-022-01891-w","article-title":"On the role of deep learning model complexity in adversarial robustness for medical images","volume":"22","author":"D Rodriguez","year":"2022","journal-title":"BMC Med Inform Decis Mak."},{"key":"pone.0342062.ref090","first-page":"1633","article-title":"On adaptive attacks to adversarial example defenses","volume":"33","author":"F Tramer","year":"2020","journal-title":"Advances in Neural Information Processing Systems."},{"issue":"5","key":"pone.0342062.ref091","article-title":"Enhancing fairness in disease prediction by optimizing multiple domain adversarial networks","volume":"4","author":"B Li","year":"2025","journal-title":"PLOS Digit Health."},{"key":"pone.0342062.ref092","unstructured":"PPMI. Parkinson\u2019s progression markers initiative. 2010. https:\/\/www.ppmi-info.org"},{"key":"pone.0342062.ref093","unstructured":"Goodfellow IJ, Shlens J, Szegedy C. Explaining and harnessing adversarial examples. arXiv preprint 2014. https:\/\/arxiv.org\/abs\/1412.6572"},{"issue":"1","key":"pone.0342062.ref094","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.11553","article-title":"Fair inference on outcomes","volume":"32","author":"R Nabi","year":"2018","journal-title":"AAAI."},{"issue":"11","key":"pone.0342062.ref095","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1001\/jamainternmed.2018.3763","article-title":"Potential biases in machine learning algorithms using electronic health record data","volume":"178","author":"MA Gianfrancesco","year":"2018","journal-title":"JAMA Intern Med."},{"issue":"2","key":"pone.0342062.ref096","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3631326","article-title":"Bias mitigation for machine learning classifiers: a comprehensive survey","volume":"1","author":"M Hort","year":"2024","journal-title":"ACM J Responsib Comput."},{"key":"pone.0342062.ref097","doi-asserted-by":"crossref","unstructured":"Chang H, Shokri R. On the privacy risks of algorithmic fairness. In: 2021 IEEE European Symposium on Security and Privacy (EuroS&P). 2021. p. 292\u2013303. https:\/\/doi.org\/10.1109\/eurosp51992.2021.00028","DOI":"10.1109\/EuroSP51992.2021.00028"},{"key":"pone.0342062.ref098","unstructured":"Grgic-Hlaca N, Zafar MB, Gummadi KP, Weller A. The case for process fairness in learning: feature selection for fair decision making. In: NIPS symposium on machine learning and the law. vol. 1. Barcelona, Spain; 2016. p. 11."},{"key":"pone.0342062.ref099","doi-asserted-by":"crossref","unstructured":"Corbett-Davies S, Pierson E, Feller A, Goel S, Huq A. Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017. p. 797\u2013806. https:\/\/doi.org\/10.1145\/3097983.3098095","DOI":"10.1145\/3097983.3098095"},{"issue":"2","key":"pone.0342062.ref100","article-title":"The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports","volume":"20","author":"G Cui","year":"2025","journal-title":"PLoS One."},{"issue":"2","key":"pone.0342062.ref101","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1089\/big.2016.0047","article-title":"Fair prediction with disparate impact: a study of bias in recidivism prediction instruments","volume":"5","author":"A Chouldechova","year":"2017","journal-title":"Big Data."}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0342062","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T18:49:55Z","timestamp":1771872595000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0342062"}},"subtitle":[],"editor":[{"given":"Rizik M. H.","family":"Al-Sayyed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,2,23]]},"references-count":101,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2,23]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0342062","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,23]]}}}