Predicting Alzheimer's Disease Using Filter Feature Selection Method

Alzheimer’s disease (AD) is caused by multiple variables. Alzheimer's development and progression are influenced genetic variants. The molecular pathways causing still poorly understood. In research, determining an effective reliable diagnosis remains a major difficulty, particularly in the early stages (i.e., Moderate Cognitive Impairment (MCI)). Researchers technologists working fields of machine learning data mining can help improve situation, AD but face hurdle when it comes to high-dimensional processing. By reducing irrelevant redundant from microarray gene expression data, technique feature selection save computing time, accuracy, encourage deeper effect on system or data. strategy described this article reduces noise well. particular, Pearson's correlation coefficient used assess redundancy. efficacy these features assessed using Support Vector Machine (SVM) classification approach. proposed approach has accuracy up 91.1 %. As result, newly established approaches for disease(AD) being improved. Index Terms— Disease, vector machine, learning, selection, Pearson’s coefficient.