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Prediction of Pathological Subjects Using Genetic Algorithms.

Murat Sari¹, Can Tuna¹

¹Department of Mathematics, Yildiz Technical University, Esenler, Istanbul 34220, Turkey.

Computational and Mathematical Methods in Medicine

|April 7, 2018

Summary

This study estimates pathological tibial rotation using physical data. Genetic algorithm (GA) outperformed K-Means (KM) clustering for large datasets, aiding health professionals in patient treatment.

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Area of Science:

Biomedical Engineering
Orthopedics
Data Science

Background:

Tibial rotation is classified into four types: RTER, RTIR, LTER, and LTIR.
Pathological tibial rotation is defined by narrow (Type 1) or wide (Type 3) angular values, while Type 2 is considered nonpathological.
Physical factors like age, weight, and height can be used to assess tibial rotation pathology.

Purpose of the Study:

To estimate pathological subjects using physical information.
To compare the effectiveness of Genetic Algorithm (GA) and K-Means (KM) clustering for this estimation.
To determine the utility of these algorithms in clinical practice for physiotherapists and orthopedists.

Main Methods:

Utilized a dataset including physical factors (age, weight, height) and tibial rotation values.
Employed the Genetic Algorithm (GA) for clustering and optimizing tibial rotation data.
Used the K-Means (KM) clustering algorithm for comparative analysis.

Main Results:

Genetic Algorithm (GA) demonstrated superior performance compared to K-Means (KM) in clustering and optimizing tibial rotation data for large populations.
K-Means (KM) showed similar effectiveness to GA for smaller datasets.
The study successfully used physical information to identify pathological tibial rotations.

Conclusions:

Genetic Algorithm (GA) is a more effective tool than K-Means (KM) for assessing pathological tibial rotation in large patient populations.
These findings can significantly assist health workers, including physiotherapists and orthopedists, in developing targeted treatment plans.
The integration of physical data with advanced algorithms offers a promising approach for clinical decision-making in orthopedic care.