Integrating Artificial Intelligence with Decision Tree Classifiers for Superior Heart Disease Detection

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 10

Year of Publication : 2024

Authors : Hardik Prajapati, Dushyantsinh B. Rathod

10.14445/23488379/IJEEE-V11I10P117

How to Cite?

Hardik Prajapati, Dushyantsinh B. Rathod, "Integrating Artificial Intelligence with Decision Tree Classifiers for Superior Heart Disease Detection," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 10, pp. 168-173, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I10P117

Abstract:

Due to the heart's crucial function as one of the most essential systems in the human body, it needs concentrated care. Given the correlation between several illnesses and cardiovascular well-being, it is important to possess precise data for forecasting such ailments. An investigation that compares different aspects of this field is essential for this objective. Many people nowadays suffer from illnesses often discovered at a late stage, mostly because of the imprecise nature of diagnostic methods. Hence, it is crucial to determine the most important data for illness prediction. The use of machine learning, an exceptionally efficient testing technique, is very pertinent in this context. Artificial intelligence operates by the use of iterative testing and training procedures. One of its subfields, called machine learning, is instructing robots to imitate human capabilities. The integration of these technologies is typically connected with the term "artificial intelligence" since they are trained to recognize and use data. In this study, we use physiological indicators such as cholesterol levels, heart rate, biological sex, and age as test data to compare the accuracy of different machine learning algorithms. Machine learning naturally learns from natural phenomena. This project specifically employs three algorithms: Gaussian Naive Bayes, Support Vector Machine, and Logistic Regression. The first segment of this article provides a comprehensive introduction to artificial intelligence and its association with heart-related concerns. The second portion explores the intricacies of the Data Mining Algorithm. The third portion examines the current body of literature. The architecture under consideration is examined in the fourth part. The fifth part provides a concise overview of the dataset and its features. The last part provides a summary and a concise examination of the future potential of the investigation.

Keywords:

Supervised, Confusion matrix, Linear regression, Unsupervised, Python, Reinforced.

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