Using Dimensionality Reduction to Diagnose Heart Diseases

12 pages•Published: May 1, 2023

Abstract

Heart disease is a major health concern in South Africa, and early and accurate diagnosis is crucial for effective treatment. In this context, dimensionality reduction techniques can play an important role. These techniques can help identify patterns and relationships in large and complex datasets, allowing for more efficient and accurate diagnoses. This paper provides an overview of the use of dimensionality reduction techniques, including principal component analysis (PCA), linear discriminant analysis (LDA), and t distributed stochastic neighbor embedding (t-SNE), in the diagnosis of heart diseases in South Africa. The paper also highlights the importance of considering the interpretability of the results, as well as potential biases in the data and algorithms, when selecting a technique. The purpose of this study is to predict the accuracy of heart disease using Dimensionality technique to determine if there was any enhance in predicting accuracy. Although the SVM show the better accuracy score of 71% over Random Forest with the score of 61% when PCA model is applied the use of dimensional reduction doesn’t produce better results.

Keyphrases: (PCA) Principal Component Analysis, dimensional reduction, Heart diseases data mining algorithm, machine learning, SVM

In: Hossana Twinomurinzi, Nkosikhona Msweli, Tendani Mawela and Surendra Thakur (editors). Proceedings of NEMISA Digital Skills Conference 2023: Scaling Data Skills For Multidisciplinary Impact, vol 5, pages 151--162

Links:	https://easychair.org/publications/paper/dRvT
	https://doi.org/10.29007/tfbr