A Comparative Analysis using various algorithm Approaches to Enhance Heart Disease Prognosis
DOI:
https://doi.org/10.4108/eetpht.10.5615Keywords:
Heart Prognosis, Machine Learning, Data Mining, Naive Bayes, SGDAbstract
INTRODUCTION: Modern advancements in technology and data science have propelled the healthcare industry towards developing more accurate disease prognostic prediction models. Heart disease, being a leading cause of mortality globally, is a critical area of focus. This study delves into enhancing heart disease prognosis through a comprehensive exploration of various algorithmic approaches.
OBJECTIVES: The objective of this paper is to compare and analyze different algorithmic techniques to improve heart disease prognosis using a dataset comprising data from over thirty thousand individuals obtained through Kaggle.
METHODS: Techniques derived from social network analysis are employed to conduct this research. Data preprocessing, feature engineering, algorithm selection (including Stochastic Gradient Descent, AdaBoosting, Support Vector Machine, and Naive Bayes), hyperparameter tuning, model evaluation, and visualization are part of the systematic research process.
RESULTS: The main results obtained in this paper include the identification of Naive Bayes as the most effective model for heart disease prognosis, followed by AdaBoosting, SVM, and Stochastic Gradient Descent. Performance evaluation metrics such as AUC, CA, F1, Precision, and Recall demonstrate the efficacy of these models.
CONCLUSION: This research contributes to improving heart disease prognosis by leveraging algorithmic techniques and thorough analysis. The study envisions integrating the developed model into healthcare systems for widespread access to accurate heart disease prediction, with future plans to enhance data collection and model improvement for better outcomes.
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Copyright (c) 2024 Anuraag Ramineni, Rishita Konda, Jayashree J, Deepak Sannapareddy, Saketh Konduri
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