Alzheimer's disease risk prediction using machine learning for survival analysis with a comorbidity-based approach

Alzheimer's disease (AD) presents a pressing global health challenge, demanding improved strategies for early detection and understanding its progression. In this study, we address this need by employing survival analysis techniques to predict transition time from Cognitive Normal (CN) to Mild Cognitive Impairment (MCI) in elderly individuals, considering the predictive value of baseline comorbidities. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) databases, we construct feature sets encompassing demographics, cognitive scores, and comorbidities. Various machine learning and deep learning methods for survival analysis are employed. Our top-performing model, fast random forest, achieves a concordance index of 0.84 when considering all feature modalities, with comorbidity data emerging as a significant predictor. The top features identified by the best-performing model include one demographic feature (age), seven cognitive scores (ADAS13, RAVLT learning, FAQ, ADAS11, RAVLT immediate, CDRSB, ADASQ4), and two comorbidities (Endocrine & Metabolic, Renal & Genitourinary). Age is highlighted as the most influential predictor, while cognitive scores are crucial indicators of Alzheimer's disease. External validation against the AIBL dataset affirms the robustness of our approach. Overall, our study contributes to a deeper understanding of the role of baseline comorbidities in AD risk prediction and emphasizes the importance of incorporating comprehensive feature assessment in clinical practice for early diagnosis and personalized treatment planning.