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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Early Prediction of Heart Disease |
| Authors Name: | Sauleh Shabir , Sufiyan Ahmed Mujawar , Sagarika Das , K Susheel Kumar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_212604
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| Published Paper Id: | IJNRD2401174 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | In the realm of healthcare and medical research, early disease prediction stands as a pivotal pillar for improving patient prognosis and curbing healthcare expenses. This study delves into the application of various machine learning algorithms like as Random Forest, Logistic Regression, Support Vector Machines (SVC), Decision Trees, and Gradient Boosting—for the accurate prediction of heart diseases. The primary goal is to develop robust predictive models that effectively analyze medical data, and contribute to timely identification and precise prognosis of cardiovascular conditions. This report synthesizes findings from an extensive literature review that scrutinizes multiple studies related to and around heart disease prediction using diverse machine learning methodologies. The analysis encompasses distinct approaches and datasets, showcasing the performance of algorithms in predicting heart diseases based on varying parameters and attributes. The comprehensive comparative analysis and evaluation conducted in this report aim to determine the superior-performing algorithms for heart disease prediction, contributing significantly to enhanced diagnostic precision and better patient care. This synthesis of various studies underscores the pivotal role of machine learning in revolutionizing healthcare, providing a roadmap for optimized models and potential real-world applications in heart disease diagnosis and prognosis. |
| Keywords: | Early Heart Disease Prediction, Machine Learning, , KNN, SVC, Logistic Regression, Decision Trees, Gradient Boosting, Random Forest, Tkinter (GUI). |
| Cite Article: | "Early Prediction of Heart Disease", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 1, page no.b643-b645, January-2024, Available :http://www.ijnrd.org/papers/IJNRD2401174.pdf |
| Downloads: | 000118752 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2401174 Registration ID: 212604 Published In: Volume 9 Issue 1, January-2024 DOI (Digital Object Identifier): Page No: b643-b645 Country: Bangalore, Karnataka, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2401174 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2401174 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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