Diagnosis Of Alzheimer’s Disease Using Machine Learning

Abstract

This project applies the paramount machine learning techniques for the early detection and effective diagnosis of severe AD. It is vital to diagnose the disease in initial stages for more effective and beneficial treatment. Machine Learning is becoming an area of great interest because it is showing the remarkable achievement in the present, advance and crucial decision making. Medical diagnosis is one of the crucial areas that have paramount importance where various learning algorithms can be contributed for the improvement in disease diagnosis. Due to the evolution of computation technology, the generation of data is increased exponentially, especially in medical field. To cope up this problem, this project tests various approaches like Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Ada boosting , to identify the best parameters for the Alzheimer’s Disease prediction using OASIS_Longitudinal MRI Data. Experimental result is analyzed in terms of accuracy, recall, and AUC (Area Under Curve). The analysis shows that the Random Forest and AdaBoost have same accuracy but Random Forest performs better than AdaBoost in terms of recall and AUC as well.