DEVELOPING AN EARLY PREDICTIVE SYSTEM FOR IDENTIFYING GENETIC BIOMARKERS ASSOCIATED TO ALZHEIMER’S DISEASE USING MACHINE LEARNING TECHNIQUES

Abstract

Alzheimer’s disease (AD) is an irreversible, progressive disorder that assaults the nerve cells of the brain. It is the most widely recognized kind of dementia among older adults. Apolipoprotein E (APOE), is one of the most common genetic risk factors for AD whose significant association with AD is observed in various genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation among individuals. SNPs related to many common diseases like AD. SNPs are recognized as significant biomarkers for this disease, they help in understanding and detecting the disease in its early stages. Detecting SNPs biomarkers associated to the disease with high classification accuracy leads to early prediction and diagnosis. Machine learning techniques are utilized to discover new biomarkers of the disease. Sequential minimal optimization (SMO) algorithm with different kernels, Naive Bayes (NB), tree augmented Naive Bayes (TAN) and K2 learning algorithm have been applied on all genetic data of Alzheimer’s disease neuroimaging initiative phase 1 (ADNI-1)/Whole genome sequencing (WGS) datasets. The highest classification accuracy was achieved using 500 SNPs based on the [Formula: see text]-value threshold ([Formula: see text]-value [Formula: see text]). In whole genome approach ADNI-1, results revealed that NB and K2 learning algorithms scored an overall accuracy of 98% and 98.40%, respectively. In whole genome approach WGS, NB and K2 learning algorithms scored an overall accuracy of 99.63% and 99.75%, respectively.