Ensemble Predictive Modeling for Dementia Diagnosis

The goal of this study is to evaluate and compare the prediction performance of three different models, Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS), in predicting dementia. The models were trained and tested using a dataset of 149 participants and nine input variables, with a $70 \% / 30 \%$ data split. Performance indicators for evaluation were $\mathrm{R}^{2}$, RMSE, and MSE. The results show that all models correctly predicted dementia, with ANN and ANFIS marginally exceeding MLR in terms of prediction accuracy. The training and testing phases yielded a perfect $\mathrm{R}^{2}$ score of 1, suggesting a strong fit to the dataset. This study contributes to dementia prediction research by providing a detailed review of various models.