GA-Based Data Mining Applied to Genetic Data for the Diagnosis of Complex Diseases

Abstract

A new algorithm is presented for finding genotype-phenotype association rules from data related to complex diseases. The algorithm was based on genetic algorithms, a technique of evolutionary computation. The algorithm was compared to several traditional data mining techniques and it was proved that it obtained better classification scores and found more rules from the data generated artificially. It also obtained similar results when using some UCI Machine Learning datasets. In this chapter it is assumed that several groups of Single Nucleotide Polymorphisms (SNPs) have an impact on the predisposition to develop a complex disease like schizophrenia. It is expected to validate this in a short period of time on real data.

Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer’s Disease

“…Identifying hidden patterns existing in a dataset was the basic step of constructing association rules for data analysis. All these studies were faced with the memory problems of runtime and computation effectively [ 17 ].…”