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The prevalence of dementia has increased over time as global life expectancyimproves and populations age. An individual's risk of developing dementia isinfluenced by various genetic, lifestyle, and environmental factors, amongothers. Predicting dementia risk may enable individuals to employ mitigationstrategies or lifestyle changes to delay dementia onset. Current computationalapproaches to dementia prediction only return risk upon narrow categories ofvariables and do not account for interactions between different risk variables.The proposed framework utilizes a novel holistic approach to dementia riskprediction and is the first to incorporate various sources of tabularenvironmental pollution and lifestyle factor data with network systemsbiology-based genetic data. LightGBM gradient boosting was employed to ensurevalidity of included factors. This approach successfully models interactionsbetween variables through an original weighted integration method coinedSysable. Multiple machine learning models trained the algorithm to reducereliance on a single model. The developed approach surpassed all existingdementia risk prediction approaches, with a sensitivity of 85%, specificity of99%, geometric accuracy of 92%, and AUROC of 91.7%. A transfer learning modelwas implemented as well. De-biasing algorithms were run on the model via the AIFairness 360 Library. Effects of demographic disparities on dementia prevalencewere analyzed to potentially highlight areas in need and promote equitable andaccessible care. The resulting model was additionally integrated into auser-friendly app providing holistic predictions and personalized riskmitigation strategies. The developed model successfully employs holisticcomputational dementia risk prediction for clinical use.

Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches