Prediction of Heart Diseases Using Data Mining Algorithms

Authors

  • Karrar Al-Jammali Karrar AL-Jammali Faculty of Pharmacy, University of Kufa, Najaf, Iraq

DOI:

https://doi.org/10.31449/inf.v47i5.4467

Abstract

Data mining has been successfully used in numerous businesses and sectors as a result of its success in great visible areas like e-commerce and marketing. Healthcare is one of the recently identified industries. Healthcare sector continues to be "information-rich." The healthcare systems have access to a multitude of data sets and can use them to find hidden links and trends in data. There aren't enough efficient analysis tools, though. The dataset is analyzed using various machine learning algorithms, i.e., decision trees, neural networks, support vector machines, and algorithms. The experiment makes use of data mining.This study paper aims to present an overview of the most recent methods for knowledge discovery in databases utilizing. Data mining is a technique used in modern medical research, especially to predict heart disease. The primary cause of a significant portion of deaths worldwide is heart disease.Several experiments on the dataset have been done to compare the performance of predictive data mining techniques. The results show that SVM performs better of Other predictive techniques, such as ANN Neural Networks, and the decision tree performs poorly.We are recommending that you test more classifiers, so you may compare the results with other algorithms and improve the system in our earlier work by adding more features. This will help the system predict and diagnose people with heart disease more accurately.

Author Biography

Karrar Al-Jammali, Karrar AL-Jammali Faculty of Pharmacy, University of Kufa, Najaf, Iraq

Computer Science ,Master in Computer Sciences from Eötvös Loránd University/ Hungary ,Assistant Lecturer, computer scientist

References

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Published

2023-05-23

How to Cite

Al-Jammali, K. (2023). Prediction of Heart Diseases Using Data Mining Algorithms. Informatica, 47(5). https://doi.org/10.31449/inf.v47i5.4467

Issue

Vol. 47 No. 5 (2023): Online-only issue

Section

Online-only

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