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An In Vitro Approach to Study Mitochondrial Dysfunction: A Cybrid Model

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IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning.

Atta-Ur Rahman¹, Muhammad Umar Nasir², Mohammed Gollapalli³

¹Department of Computer Science (CS), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

Computational Intelligence and Neuroscience

|August 1, 2022

Summary

This summary is machine-generated.

This study introduces a machine learning model for predicting genetic disorders using patient medical history. The Support Vector Machine (SVM) algorithm demonstrated superior accuracy compared to K-Nearest Neighbor (KNN) and other methods.

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Area of Science:

Genetics
Computational Biology
Medical Informatics

Background:

Genetic disorders pose a significant global health challenge, necessitating accurate and early detection methods.
Existing prediction models often rely on genome sequencing, with limited exploration of patient medical history.
Early identification of genetic conditions is vital for effective management and improving patient outcomes.

Purpose of the Study:

To develop and evaluate a novel Internet of Medical Things (IoMT)-based model for predicting mitochondrial and multifactorial genetic disorders.
To compare the predictive performance of Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms using patient medical history data.
To enhance the accuracy of genetic disorder prediction by leveraging machine learning on clinical data.

Main Methods:

Utilized two distinct machine learning algorithms: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).
Developed a model integrating the Internet of Medical Things (IoMT) for data acquisition and analysis.
Trained and tested the models using datasets derived from a large compilation of patient medical reports.

Main Results:

The Support Vector Machine (SVM) algorithm achieved higher accuracy in predicting genetic disorders compared to the K-Nearest Neighbor (KNN) algorithm.
SVM demonstrated a training accuracy of 94.99% and a testing accuracy of 86.6%.
The proposed IoMT-based model, particularly with SVM, outperformed existing prediction methods that rely on patient history.

Conclusions:

Machine learning, specifically SVM, offers a promising approach for accurate genetic disorder prediction using patient medical history.
The IoMT integration facilitates robust data handling for clinical prediction models.
Further research utilizing patient medical history can improve the accuracy and accessibility of genetic disorder diagnostics.