Performance Comparison of Five Machine Learning Algorithms for Early Detection of Alzheimer's Disease
DOI:
https://doi.org/10.57152/ijatis.v3i1.2498Keywords:
Alzheimer's Disease, Classification, Early Detection, Machine Learning, XGBoostAbstract
Alzheimer's disease (AD) is the most common form of dementia and is characterized by progressive cognitive decline. Early detection of AD is crucial for earlier intervention, as there is currently no cure for this disease. This study evaluates the performance of five machine learning algorithms, namely Logistic Regression, Decision Tree, Support Vector Machine (SVM), Random Forest, and XGBoost for AD classification using a dataset of demographic information, lifestyle, medical factors, and cognitive symptoms of patients. The data was processed through pre-processing steps (data cleaning, missing value imputation, and feature selection) and model evaluation using k-fold cross-validation with a 70:30, 80:20, and 90:10 data split. Unlike several previous studies that only conducted partial evaluations, this study directly tested the performance (head-to-head) of five algorithms representing various classification paradigms.The model evaluation also focused on maximizing Recall (Sensitivity) to minimize the critical risk of false negative diagnoses in the early detection process. The results showed that the XGBoost algorithm performed best across all evaluation metrics. With an 80:20 data split, XGBoost achieved the highest performance with Accuracy, Precision, and Recall of 95.1%. These findings demonstrate the effectiveness of the XGBoost algorithm in classifying patients and support the development of faster and more objective medical decision support systems. These results have practical implications that the ML model has the potential to support clinical decision support systems for the early detection of Alzheimer's disease
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