Alzheimer Disease MRI Preprocessed Images: A Machine Intelligent Based Approach for Classification and Analysis

Purpose: Alzheimer’s disease (AD) is considered as one of the most dangerous diseases in present scenario. It a brain disorder which leads to destruction thinking skills and memory human beings. very much essential for early classification AD magnetic resonance imaging (MRI) preprocessed images (ADMPIs) into several categories such Mild_Demented (MID), Moderate_Demented (MOD), Non_Demented (ND), Very_Mild_Demented (VMD), etc. so that preventive measures can be taken at earliest. Approach: In this work, machine intelligent (MI) based approach proposed ADMPIs MID, MOD, ND VMD types. This focused on learning (ML) methods Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS), Neural Network (NNT), Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) Stochastic Gradient Descent (SGDC) carry out classification. Result: The ML have been implemented using Python Orange 3.26.0. 1564 having 500, 64, 500 numbers each type respectively are from Kaggle source. performance all assessed parameters accuracy (CA), F1, Precision (PR) Recall (RC). From results, it found NNT method capable providing better results terms CA, PR RC compared other SVMN, RFS, NNT, DTR, ADB, NBY, KNNH SGD. Originality: MI types performs LRG, SGDC methods. Paper Type: Conceptual Research.