Increased accuracy of prediction hepatitis disease using the application of principal component analysis on a support vector machine

Abstract

Data mining has been widely used to diagnose diseases from medical data. Classification is a data mining technique that can be used to predict disease. In previous studies, a support vector machine was widely used to obtain high accuracy in predicting hepatitis. In this study, the principal component analysis was applied to the support vector machine. A principal component analysis is used to extract features and reduce the number of features or attributes. Principal component analysis can reduce data dimensions without removing important information from the dataset. The extracted and reduced data are then used to classify the support vector machine. Classification performance measurement is done by using a confusion matrix. Hepatitis prediction accuracy achieved was 93.55%. This result is better than the support vector machine classification results without the application of principal component analysis.

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

“…Unsupervised ML may also be utilized for anomaly identification, such as clustering [ 132 , 133 ]. Prediction of cardiac illnesses using clustering [ 134 ] and prediction of hepatitis disease using principal component analysis (PCA), a dimensionality reduction approach [ 135 , 136 ] are two classic instances of unsupervised ML techniques in healthcare.…”

Peningkatan Akurasi Algoritma Support Vector Machine Menggunakan Dual-Tree Complex Wavelet Transform Pada Klasifikasi Citra Katarak dan Normal

“…Klasifikasi ialah teknik yang dapat digunakan untuk memprediksi data atau menggambarkan kelas data. Algoritma klasifikasi dapat digunakan untuk membantu para ahli medis dalam mendiagnosis suatu penyakit (Alamsyah & Fadila, 2021). Support vector machine (SVM) ialah salah satu metode klasifikasi pembelajaran terbimbing atau disebut supervised learning.…”

Eigenvalue Distributions in Random Confusion Matrices: Applications to Machine Learning Evaluation

“…The distribution of eigenvalues provides a quantitative measure of the spread and concentration of information in the matrix. Understanding the distribution of eigenvalues of a confusion matrix can be valuable for various purposes, including model assessment, variable selection, high-dimensional analysis, dimension reduction, model comparison, anomaly detection, and generalization or overfitting issues [1,[11][12][13][14][15].…”

“…In a similar context, ref. [13] increased the performance of support vector machine (SVM) by employing eigenvalue analysis of the features covariance matrices and subsequently performing PCA to reduce the dimension of the features. This approach helps to increase the prediction accuracy of hepatitis disease.…”