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An Efficient PCA-GA-HKSVM-Based Disease Diagnostic Assistant.

Brenda Jerop¹, Davies Rene Segera¹

¹Department of Electrical and Information Engineering, University of Nairobi, Kenya.

Biomed Research International

|November 1, 2021

Summary

This study introduces a novel machine learning approach, PCA-GA-HKSVM, to enhance disease diagnosis accuracy. The hybrid model significantly improves upon traditional methods for faster and more reliable medical diagnoses.

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Area of Science:

Medical Informatics
Machine Learning in Healthcare
Computational Biology

Background:

Disease diagnosis is hindered by misdiagnosis, delays, and missed diagnoses.
Machine learning classification models use symptoms to predict disease presence, but performance can be improved.

Purpose of the Study:

To present an improved machine learning technique for disease diagnosis.
To enhance classification model performance using feature selection and hybrid kernel methods.

Main Methods:

Feature selection via Principal Component Analysis (PCA).
A hybrid kernel-based Support Vector Machine (HKSVM) combining Radial Basis Function (RBF), linear, and polynomial kernels.
Hyperparameter optimization using a Genetic Algorithm (GA).
The combined PCA-GA-HKSVM model was evaluated on 7 medical datasets (2 multiclass, 5 binary).

Main Results:

The PCA-GA-HKSVM demonstrated superior performance compared to single-kernel Support Vector Machines (SVMs).
Evaluations using accuracy, precision, and recall metrics confirmed the model's effectiveness.
Combining local (RBF) and global (linear, polynomial) kernels improved model performance by better distinguishing data points at different ranges.

Conclusions:

The proposed PCA-GA-HKSVM technique offers a significant advancement in disease diagnostic accuracy.
Hybrid kernel approaches in machine learning can effectively address complex medical diagnostic challenges.
This method provides a more robust and reliable tool for medical diagnosis systems.