Predicting Genetic Disorders: Implementation and Deployment on EC2 instances in AWS

Genetic illnesses, caused by DNA mutations— either inherited or acquired—can lead to serious illnesses such as Alzheimer's, cancer, and Hemochromatosis. New developments in artificial intelligence have been promising for early disease detection. In this paper, we address the issue of predicting multiple genetic illnesses by suggesting two primary approaches: (1) a novel feature engineering approach that combines class probabilities from Extra Trees and Random Forest models, and (2) a classifier chain method where predictions from previous models impact subsequent ones. These approaches combined are intended to enhance early and precise detection of genetic conditions.

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Publication Details

Published
2025-05-02
DOI
https://doi.org/10.38124/ijisrt/25apr1261
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Predicting Genetic Disorders: Implementation and Deployment on EC2 instances in AWS

Arun Prakash, M. Prabu, Roba Abbas, Shoaib Khan
Data Mining Algorithms and Applications
article

Predicting Genetic Disorders: Implementation and Deployment on EC2 instances in AWS

Arun Prakash, M. Prabu, Roba Abbas, Shoaib Khan
article en

Abstract

Genetic illnesses, caused by DNA mutations— either inherited or acquired—can lead to serious illnesses such as Alzheimer's, cancer, and Hemochromatosis. New developments in artificial intelligence have been promising for early disease detection. In this paper, we address the issue of predicting multiple genetic illnesses by suggesting two primary approaches: (1) a novel feature engineering approach that combines class probabilities from Extra Trees and Random Forest models, and (2) a classifier chain method where predictions from previous models impact subsequent ones. These approaches combined are intended to enhance early and precise detection of genetic conditions.

Openalex Percentile: Top 87%
Data Mining Algorithms and Applications
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