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

Disease diagnosis faces challenges such as misdiagnosis, lack of diagnosis, and slow diagnosis. There are several machine learning techniques that have been applied to address these challenges, where a set of symptoms is applied to a classification model that predicts the presence or absence of a di...

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Bibliographic Details
Published in:BioMed research international 2021-10, Vol.2021, p.4784057-10
Main Authors: Jerop, Brenda, Segera, Davies Rene
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
Classification
Datasets
Diagnosis
Diagnosis, Differential
Diagnostic Techniques and Procedures - instrumentation
Early Diagnosis
Genetic algorithms
Genetic vectors
Health aspects
Humans
Kernel functions
Kernels
Learning algorithms
Machine Learning
Methods
Neural networks
Optimization
Performance evaluation
Polynomials
Principal Component Analysis - methods
Principal components analysis
Radial basis function
Signs and symptoms
Support Vector Machine
Support vector machines
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Summary:Disease diagnosis faces challenges such as misdiagnosis, lack of diagnosis, and slow diagnosis. There are several machine learning techniques that have been applied to address these challenges, where a set of symptoms is applied to a classification model that predicts the presence or absence of a disease. To improve on the performance of these techniques, this paper presents a technique which involves feature selection using principal component analysis (PCA), a hybrid kernel-based support vector machine (HKSVM) classification model and hyperparameter optimization using genetic algorithm (GA). The HKSVM in this paper introduces a new way of combining three kernels: Radial basis function (RBF), linear, and polynomial. Combining local (RBF) and global (linear and polynomial) kernels has the effect of improved model performance. This is because the local kernels are better able to distinguish points closer to each other while the global kernels are more suited to distinguish points that are far away from each other. The PCA-GA-HKSVM is used on 7 different medical datasets, with two datasets being multiclass datasets and 5 datasets being binary. Performance evaluation metrics used were accuracy, precision, and recall. It was observed that the PCA-GA-HKSVM offered better performance than the single kernel support vector machines (SVMs).
ISSN:2314-6133
2314-6141
DOI:10.1155/2021/4784057