{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:59:36Z","timestamp":1791928776319,"version":"4.3.4"},"reference-count":38,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010221","name":"Higher Education Commision, Pakistan","doi-asserted-by":"publisher","award":["16052\/NRPU\/R&D\/HEC\/2021-2020"],"award-info":[{"award-number":["16052\/NRPU\/R&D\/HEC\/2021-2020"]}],"id":[{"id":"10.13039\/501100010221","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>With a clinical trial failure rate of 99.6% for Alzheimer\u2019s Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promising results in early AD prediction, with survival ML models outperforming typical classifiers by providing probabilities of disease progression over time. This study utilized various ML survival models to predict the time-to-conversion to AD for early (eMCI) and late (lMCI) Mild Cognitive Impairment stages, considering their different progression rates. ADNI data, consisting of 291 eMCI and 546 lMCI cases, was preprocessed to handle missing values and data imbalance. The models used included Random Survival Forest (RSF), Extra Survival Trees (XST), Gradient Boosting (GB), Survival Tree (ST), Cox-net, and Cox Proportional Hazard (CoxPH). We evaluated cognitive, cerebrospinal fluid (CSF) biomarkers, and neuroimaging modalities, both individually and combined, to identify the most influential features. Our results indicate that RSF outperformed traditional CoxPH and other ML models. For eMCI, RSF trained on multimodal data achieved a C-Index of 0.90 and an IBS of 0.10. For lMCI, the C-Index was 0.82 and the IBS was 0.16. Cognitive tests showed a statistically significant improvement over other modalities, underscoring their reliability in early prediction. Furthermore, RSF-generated individual survival curves from baseline data facilitate clinical decision-making, aiding clinicians in developing personalized treatment plans and implementing preventive measures to slow or prevent AD progression in prodromal stages.<\/jats:p>","DOI":"10.1371\/journal.pone.0314725","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T18:33:58Z","timestamp":1734114838000},"page":"e0314725","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":9,"title":["Random survival forest model for early prediction of Alzheimer\u2019s disease conversion in early and late Mild cognitive impairment stages"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1959-2316","authenticated-orcid":true,"given":"Amna","family":"Saeed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0190-0700","authenticated-orcid":true,"given":"Asim","family":"Waris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Fuwad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javaid","family":"Iqbal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jawad","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dokhyl","family":"AlQahtani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omer","family":"Gilani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umer Hameed","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for The Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"pone.0314725.ref001","article-title":"Alzheimer\u2019s Facts and Figures Report | Alzheimer\u2019s Association","year":"2024","journal-title":"Accessed"},{"key":"pone.0314725.ref002","article-title":"\u201cNumbers of people with dementia worldwide: An update to the estimates in the World Alzheimer Report 2015,\u201d Nov. 2020, Accessed","author":"A. D. International","year":"2024"},{"key":"pone.0314725.ref003","article-title":"Alzheimer\u2019s disease drug-development pipeline: few candidates, frequent failures | Alzheimer\u2019s Research & Therapy | Full Text","year":"2024","journal-title":"Accessed"},{"issue":"9","key":"pone.0314725.ref004","doi-asserted-by":"crossref","DOI":"10.3390\/s23094184","article-title":"Revolutionizing the Early Detection of Alzheimer\u2019s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning","volume":"23","author":"A. G. Vrahatis","year":"2023","journal-title":"Sensors"},{"key":"pone.0314725.ref005","article-title":"Estimating Alzheimer\u2019s Disease Progression Rates from Normal Cognition Through Mild Cognitive Impairment and Stages of Dementia\u2014PMC","year":"2024","journal-title":"Accessed"},{"issue":"4","key":"pone.0314725.ref006","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1111\/j.1600-0447.2008.01326.x","article-title":"Rate of progression of mild cognitive impairment to dementia\u2014meta-analysis of 41 robust inception cohort studies","volume":"119","author":"A. J. Mitchell","year":"2009","journal-title":"Acta Psychiatr. Scand."},{"key":"pone.0314725.ref007","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyt.2019.00572","article-title":"Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI","volume":"10","author":"T. Zhang","year":"2019","journal-title":"Front. Psychiatry"},{"issue":"1","key":"pone.0314725.ref008","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.jalz.2012.09.017","article-title":"AD dementia risk in late MCI, in early MCI, and in subjective memory impairment","volume":"10","author":"F. Jessen","year":"2014","journal-title":"Alzheimers Dement."},{"key":"pone.0314725.ref009","article-title":"Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay","volume":"13","author":"N.-H. Ho","year":"2023","journal-title":"Sci. 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Med."},{"key":"pone.0314725.ref026","doi-asserted-by":"crossref","first-page":"103496","DOI":"10.1016\/j.jbi.2020.103496","article-title":"A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models","volume":"108","author":"E. Longato","year":"2020","journal-title":"J. Biomed. Inform."},{"key":"pone.0314725.ref027","article-title":"Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches)\u2014PMC","year":"2024","journal-title":"Accessed"},{"key":"pone.0314725.ref028","first-page":"1","article-title":"Permutation feature importance-based fusion techniques for diabetes prediction","author":"S. Nirmalraj","year":"2023","journal-title":"Soft Comput."},{"key":"pone.0314725.ref029","author":"A Combined Measure of Cognition and Function for Clinical Trials: The Integrated Alzheimer\u2019s Disease Rating Scale (iADRS)\u2014PMC.\u201d","year":"2024","journal-title":"Accessed"},{"key":"pone.0314725.ref030","author":"Predicting Alzheimer\u2019s disease based on survival data and longitudinally measured performance on cognitive and functional scales\u2014ScienceDirect","year":"2024","journal-title":"Accessed"},{"issue":"4","key":"pone.0314725.ref031","article-title":"Individual Survival Distributions Generated by Multi-Task Logistic Regression Yield a New Perspective on Molecular and Clinical Prognostic Factors in Gastric Adenocarcinoma","volume":"16","author":"D. 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