A Comparative Study on Forecasting Heart Diseases with Machine Learning Algorithms
T. Tamilselvi
, Batini Dhanwanth , Bandi Vivek , A Lingesh , Shaik Mohammad Waseem
Heart attack, Machine learning, random forest, logistic regression, support vector machine and gradient boosting.
One of the most prevalent reasons for death globally is a heart attack. The risk factors associated with heart attack are age, gender, family history, hypertension, sedentary lifestyle, obesity, high cholesterol levels, smoking, and diabetes. This research paper intends to progress a machine learning (ML) replica for spotting the risk of a heart attack. The Cleveland Heart Disease sample, which has 303 occurrences and 14 characteristics, was used in this study. The ML algorithms used for this study are decision tree, support vector machine (SVM), logistic regression and random forest. The results reveal that the SVM algorithm estimates the possibility of an attack with a high efficacy of 85%.
"A Comparative Study on Forecasting Heart Diseases with Machine Learning Algorithms", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.157-161, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001024.pdf
Volume 11
Issue 2,
February-2024
Pages : 157-161
Paper Reg. ID: TIJER_151003
Published Paper Id: TIJERB001024
Downloads: 000287
Research Area: Science and Technology
Country: TIRUVALLUR, TAMILNADU, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
