Machine Learning Approaches for Prediction of Alzheimer’s Disease

Authors : Kadriye Filiz Balbal

Pages : 110-118

View : 4 | Download : 1

Publication Date : 2024-12-27

Article Type : Research Paper

Abstract :Alzheimer\\\'s Disease (AD) is a disorder that significantly impacts an individual’s behavior, memory, and cognitive functions, ultimately leading to a loss of independence. Early and accurate diagnosis of AD is critical to mitigating its progression and improving patient outcomes, especially as no definitive cure is currently available. This study investigates the application of machine learning algorithms to predict and diagnose AD based on patient symptoms and clinical data. The dataset used in this research includes comprehensive health information from 2,149 patients, with 35 features covering demographic, lifestyle, and medical factors, and no missing values. Seven widely recognized machine learning algorithms—KNN, GNB, SVM, DT, RF, AdaBoost, and XGBoost—were evaluated to determine their effectiveness in disease prediction. Performance was assessed using recall, precision, accuracy, and F1-score metrics, providing a robust evaluation of each model. XGBoost achieved the highest accuracy rate of 95.35%, highlighting its superior predictive capability, while KNN recorded the lowest accuracy at 75.54%. The results demonstrate the strength of machine learning algorithms, particularly ensemble methods like XGBoost, in analyzing complex clinical data for the early detection of Alzheimer’s Disease. These findings underscore the critical role of machine learning in enhancing diagnostic accuracy and enabling timely interventions, which are essential for improving the quality of life for individuals at risk of Alzheimer’s Disease.
Keywords : makine öğrenmesi, sınıflandırma, hastalık tahmini, Alzheimer Hastalığı