Comparison of Classification Algorithms for Alzheimer’s Disease Prediction
Harshit Saxena, Divyansh Joshi, Hari Singh, Rohit Anand
Abstract
The application of machine learning to investigate metabolic disorders like diabetes and Alzheimer's Disease (AD), which impact significant populations of people worldwide, is currently receiving a lot of attention. Early on, AD was challenging to predict. The method of identifying the parameters that were most effective for predicting Alzheimer's disease involved the use of decision trees, support vector machines, XGBoost, random forest, extra trees, AdaBoost, gradient boosting, and voting classifiers. In this research, the Extra Trees classifier has been used which, from our research, has rarely been explored in the past. This classifier gives the best results in terms of performance among all the implemented techniques. The performance is evaluated using evaluation parameters such as precision, recall, F1-Score and accuracy. Using these machine learning methods, especially the Extra Trees algorithm, for early identification of Alzheimer’s disease will greatly benefit in lowering the annual mortality rate caused by AD.