READi-Dem: ML-powered, web-interface tool for Robust, Efficient, Affordable Diagnosis of Dementia

BackgroundDementia screening tools typically involve face-to-face cognitive testing. Indeed, this introduces an increasing burden on the clinical staff, particularly in low-resource settings. The objective of our study is to develop an integrated online platform for efficient dementia screening, using a brief and cost-effective assessment. MethodsWe used the Longitudinal Ageing Study in India dataset (LASI-DAD, n=2528) to predict dementia diagnosis based on the Clinical Dementia Rating (CDR). Using feature selection algorithms and principal component analysis (PCA), we identified key predictive features. We compared the performance of six machine learning (ML) classifiers that were trained on the 42 selected features (full model) and the two components identified by PCA (minimal model). The best-performing model was selected for our web platform. ResultsSelected features mapped onto two distinct, interpretable domains: a cognitive domain and an informant domain. The first two principal components cumulatively explained 90.2% of the variance and included questions from the Mini-Mental State Exam (MMSE) and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). Classifiers trained on the minimal model performed on par with the full model, with Support Vector Machine performing best (93.4%). The model did not reliably predict Parkinsons disease (67% accuracy) or stroke (53.1% accuracy), suggesting dementia specificity. The respective questions from MMSE and IQCODE (27 items) were incorporated into our online platform. ConclusionWe built an online platform enabling end-to-end screening for dementia from assessment to prediction, based on patient and caregiver reports. Web App code is available at GitHub: https://github.com/sanjaysinghrathi/READi-Dem & Web App link is available at Web Page: https://researchmind.co.uk/readi-dem. For the convenience of researchers, a video summarizing our work is also accessible on the Web App Page and YouTube Link.