Application of approximation algorithms to the detection and categorization of diseases

Abstract

The diagnosis of diseases can be formulated as a classification problem, making it an NP-hard problem. This is the case for the two problems that this work aims to solve: the classification of tumor samples from patients suspected of having breast cancer as benign or malignant and the classification of samples from patients suspected of having type II diabetes as negative or positive. In order to make accurate diagnoses (classification) of these disorders, our idea is to construct approximate algorithms based on multilayer perceptrons, genetic algorithms, and algorithms that hybridize these alternatives. Numerical experiments enable assessing and contrasting the effectiveness of various approaches using actual data sets. The results demonstrate that, in addition to outperforming algorithms suggested in the literature in terms of performance, our ideas produce outcomes with classification errors that are nearly zero.