A Novel Approach to Detection of Alzheimer’s Disease from Handwriting: Triple Ensemble Learning Model

Authors : Hakan Öcal

Pages : 214-223

Doi:10.29109/gujsc.1386416

View : 64 | Download : 153

Publication Date : 2024-03-25

Article Type : Research Paper

Abstract :The irreversible degeneration of nerve cells in the body dramatically affects the motor skills and cognitive abilities used effectively in daily life. There is no known cure for neurodegenerative diseases such as Alzheimer’s. However, in the early diagnosis of such diseases, the progression of the disease can be slowed down with specific rehabilitation techniques and medications. Therefore, early diagnosis of the disease is essential in slowing down the disease and improving patients’ quality of life. Neurodegenerative diseases also affect patients’ ability to use fine motor skills. Losing fine motor skills causes patients’ writing skills to deteriorate gradually. Information about Alzheimer’s disease can be obtained based on the deterioration in the patient’s writing skills. However, manual detection of Alzheimer’s disease (AD) from handwriting is a time-consuming and challenging task that varies from physician to physician. Machine learning-based classifiers are extremely popularly used with high-performance scores to solve the challenging manual detection of AD. In this study, Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) machine learning classification algorithms were combined with a Voting Classifier and trained and tested on the publicly available DARWIN (Diagnosis Alzheimer’s With haNdwriting) dataset. As a result of the experimental studies, the proposed Ensemble methodology achieved 97.14% Acc, 95% Prec, 100% Recall, 90.25% Spec, and 97.44% F1-score (Dice) performance values. Studies have shown that the proposed work is exceptionally robust.
Keywords : Neurodegenerative disease, Alzheimers disease prediction, ensemble machine learning model, classification, handwriting data