Predictive Analytics for Dementia: Machine Learning on Healthcare Data

TL;DR

This paper demonstrates that machine learning models, especially LDA, can predict dementia with high accuracy using healthcare data, emphasizing interpretability and feature importance.

Contribution

It applies supervised ML techniques with data balancing and feature extraction to improve dementia prediction accuracy and interpretability.

Findings

LDA achieved 98% accuracy in dementia prediction

Feature importance includes APOE-epsilon4 and diabetes

Model interpretability is crucial for clinical adoption

Abstract

Dementia is a complex syndrome impacting cognitive and emotional functions, with Alzheimer's disease being the most common form. This study focuses on enhancing dementia prediction using machine learning (ML) techniques on patient health data. Supervised learning algorithms are applied in this study, including K-Nearest Neighbors (KNN), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Gaussian Process Classifiers. To address class imbalance and improve model performance, techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization were employed. Among the models, LDA achieved the highest testing accuracy of 98%. This study highlights the importance of model interpretability and the correlation of dementia with features such as the presence of the APOE-epsilon4 allele and chronic conditions like diabetes. This research advocates for future ML innovations, particularly in integrating explainable AI approaches, to further improve predictive capabilities in dementia care.