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2024 | OriginalPaper | Chapter

Classification of DNA Sequence for Diabetes Mellitus Type Using Machine Learning Methods

Authors : Lena Abed AL Raheim Hamza, Hussein Attia Lafta, Sura Zaki Al Rashid

Published in: Micro-Electronics and Telecommunication Engineering

Publisher: Springer Nature Singapore

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Abstract

High blood sugar levels in diabetes mellitus (DM) can cause cardiac arrest, nervous system damage, vision loss, foot problems, liver or kidney damage, and death if left untreated. Age, gender, family history, BMI, and glucose levels all contribute to diabetes. To increase diabetes detection and prevent health concerns, machine learning techniques are used for prediction. Identifying the type of diabetes and considering the risk of accompanying diseases can improve diabetes prediction accuracy. This study uses one-way analysis of variance, mutual information, and F-regressor with random forest, Gaussian Naive Bayes, support vector machine, and decision tree for feature selection. Results with and without selected algorithms are compared. They have been used to adjust diabetic care using clinical parameters like accuracy, precision, recall, and F1-score. Random forest (RF) using F-regressor (FR) or ANOVA feature selection and numerous iterations of N (75) and K (3–5) outperforms competitors with 0.9 accuracy. This proves the diabetes-related DNA sequence classification technique works.

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Title: Classification of DNA Sequence for Diabetes Mellitus Type Using Machine Learning Methods
Authors: Lena Abed AL Raheim Hamza
Hussein Attia Lafta
Sura Zaki Al Rashid
Publisher: Springer Nature Singapore
Book: Micro-Electronics and Telecommunication Engineering
Print ISBN: 978-981-9995-61-5

Electronic ISBN: 978-981-9995-62-2

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-99-9562-2_8