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

Abstract

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).

Neural network models selection scheme for health mobile app development

“…The results of this work are compared to a work by Jerop and Segera [29] that combines principal component analysis (PCA) as feature selection, a hybrid kernel-based support vector machine (HKSVM) classification model, and hyperparameter optimization using a genetic algorithm (GA). The proposed HKSVM Int J Artif Intell ISSN: 2252-8938  Neural network models selection scheme for health mobile app development (Yaya Sudarya Triana) 1199 combines three kernels: linear, polynomial, and radial basis function (RBF), with the aim of improving accuracy performance.…”

SVPC-LDA: A Hybridised Feature Extraction Approach for Chronic Kidney Disease Dataset

“…Accuracy, sensitivity, speci city, and ROC curve parameters were evaluated, and it was nally concluded that non-linear DRT performed better as compared to linear approaches. In order to improve the performance of the classi cation system, hyperparameter optimization tech-niques were integrated with the kernel support vector machine classi er in 2021 by [14]. In 2020, a comparative analysis of commonly used dimensionality reduc-tion techniques was presented [17].…”