Design and Validation of a Hybrid Machine Learning Model for Alzheimer's Detection Using Handwriting Data

Handwriting is a preferred identifier in detecting Alzheimer's disease that enables diagnosis about people. The aim of this study is to evaluate the handwriting and make the early detection and diagnosis of Alzheimer's disease with the highest possible prediction rates. In this regard, 9 machine learning algorithms were used. Seven feature selection methods were used to determine the most effective features for Alzheimer's disease prediction to eliminate unnecessary ones and increase model prediction performance. The models were trained and tested on the DARWIN dataset with both train - test split and cross-validation methods. According to the results, it has been evaluated that the highest performance criterion values are generally achieved when the SHAP is used as the feature selection method. According to the results, the appropriate model that achieved the highest performance values was determined as the hybrid SHAP-Support Vector Machine model with 0.9623 accuracy, 0.9643 precision, 0.9630 recall and 0.9636 F1-Score.