Chapter 7
Utilizing Data Mining Classification Algorithms for Early Diagnosis of Heart Diseases
Ahmad Mousa Altamimi,
Mohammad Azzeh,
Ahmad Mousa Altamimi
Search for more papers by this authorMohammad Azzeh
Search for more papers by this authorAhmad Mousa Altamimi,
Mohammad Azzeh,
Ahmad Mousa Altamimi
Search for more papers by this authorMohammad Azzeh
Search for more papers by this authorBook Editor(s):Kishor Kumar Sadasivuni, Hassen M. Ouakad, Somaya Al-Maadeed, Huseyin C. Yalcin, Issam Bait Bahadur,
First published: 22 April 2022
Abstract
Heart disease diagnosis is based on the patient's signs and symptoms and is affected by several factors such as cholesterol level, blood pressure, obesity, smoking habit, and other factors. This chapter focuses on data mining classification techniques for predicting heart diseases. Five classifiers (Naive Bayes (NB), support vector machine, random forest, decision tree, and linear discriminant analysis) have been used to analyze a medical data set recorded to diagnose cardiovascular diseases. The association rules mining technique, for example, has been utilized in many works to find frequent items among large patient data sets to diagnose the presence of heart diseases. Classification methods have been extensively applied in developing prediction models for heart diseases. Machine learning algorithms are widely used to extract valuable knowledge from hidden relationships and trends among the data. In traditional healthcare systems, doctors rely on the signs or symptoms of patients to diagnose heart diseases.
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