Ensemble Based Heart Disease Prediction Using Machine Learning

Minchala Siva Krishna and K. Naveen

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 4, 184-190

Abstract: Clinical science has garnered significant attention from researchers due to their efforts in identifying early human mortality causes. The literature has confirmed that diseases can be caused by various factors, including heart-based disorders. To save human lives and assist healthcare professionals in recognizing, preventing, and managing heart disease, numerous researchers have proposed specific techniques.These techniques include the use of decision trees, random forests, XGBoost, and crossover models. The proposed approach dynamically analyzes the performance of each method, starting with the selection of the most appropriate strategy. The analysis involves implementing these approaches with different features to examine the statistics comprehensively. However, it is important to note that each successful plan has its own limitations. The goal is to build an intelligent and effective method through careful examination and refinement.

Keywords: Machine learning; Classification Technique; Decision Tree; Random Forest Stacking Classifier; supervised machine learning. (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2390252
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