Machine Learning-Based Multi-Factorial Genetic Disorder Prediction System

Abstract

A genetic disorder is a medical illness caused by an error in a person's DNA. Genes are hereditary units that carry instructions for the body's development, functioning, and upkeep. Mutations or alterations in these genes can cause genetic illnesses, affecting how the body's cells create proteins or carry out certain functions. This study proposed the multi-factorial genetic disorder prediction system using machine learning algorithms specifically Decision trees and Naïve Bayes. The study covered three diseases cancer, cystic fibrosis and diabetes. Diabetes, cystic fibrosis, and cancer are frequently the result of a complicated interaction of genetic, environmental, and behavioural variables. This proposed model can identify those who are more likely to develop certain diseases, allowing for early intervention and personalized preventive care. These proactive healthcare methods not only improve patient outcomes but also contribute to the general efficiency and efficacy of the healthcare system, supporting a change towards more targeted and efficient healthcare delivery.