Paper Title
Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
Article Identifiers
Registration ID: IJNRD_218985
Published ID: IJNRD2404581
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Keywords
Machine Learning, Logistic Regression, Gradient Boosting Algorithm, Hybrid machine learning algorithms.
Abstract
— The diagnosis of heart disease is the most difficult task in the medical field. The diagnosis of heart disease is difficult as a decision relied on grouping of large clinical and pathological data. Due to this complication, the interest increased in a significant amount between the researchers and clinical professionals about the efficient and accurate heart disease prediction. In case of heart disease, the correct diagnosis in early stage is important as time is the very important factor. Heart disease is the principal source of deaths widespread, and the prediction of heart disease is significant at an untimely phase. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. The main idea behind this work is to study diverse prediction models for the heart disease and selecting important heart disease feature using hybrid machine learning algorithm. Logistic regression and gradient boosting are Machine Learning algorithm which has the high accuracy compared to other Supervised Machine Learning algorithms. By using logistic regression and gradient boosting algorithm, we are going to predict if a person has heart disease or not.
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How To Cite (APA)
Tanaya Chaporkar, Mayuresh Khanzode, Hemant Malpani, Tushar Joshi, & Prof. H. D. Misalkar (April-2024). Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), f667-f678. https://ijnrd.org/papers/IJNRD2404581.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : f667-f678
Other Publication Details
Paper Reg. ID: IJNRD_218985
Published Paper Id: IJNRD2404581
Research Area: Information TechnologyÂ
Author Type: Indian Author
Country: Amravati, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404581.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404581
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