Research Paper | Computer Science & Engineering | India | Volume 9 Issue 5, May 2020
Cardiovascular Disease Prediction using Classification Algorithms of Machine Learning
Yash Jayesh Chauhan
Cardiovascular disease is a major health burden worldwide in the 21st century. Human services consumptions are overpowering national and corporate spending plans because of asymptomatic infections including cardiovascular ailments. Consequently, there is an urgent requirement for early location and treatment of such ailments. The information which is gathered by data analysis of hospitals is utilizing by applying different blends of calculations and algorithms for the early-stage prediction of Cardiovascular ailments. Machine Learning is one of the slanting innovations utilized in numerous circles far and wide including the medicinal services application for predicting illnesses. In this research, we compared the accuracy of machine learning algorithms that could be used for predictive analysis of heart diseases and predicting the overall risks. The proposed experiment is based on a combination of standard machine learning algorithms such as Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), support vector machine (SVM) and Decision Tree. Most of the entities in this world are related in one way or another, at times finding a relationship between entities can help you make valuable decisions. Likewise, I will attempt to utilize this information as a model that predicts the patient whether they are having a Cardiovascular disease or on the other hand not. Moreover, the data analysis is carried out in Python using Jupyter Lab in order to validate the accuracy of all the Algorithm.
Keywords: Machine learning, Data Analysis, Classification algorithms, Heart diseases
Edition: Volume 9 Issue 5, May 2020
Pages: 194 - 200
How to Cite this Article?
Yash Jayesh Chauhan, "Cardiovascular Disease Prediction using Classification Algorithms of Machine Learning", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=SR20501193934, Volume 9 Issue 5, May 2020, 194 - 200
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