Comparison and Evaluation of Various Machine Learning Algorithms on Heart Disease Data Set
DOI:
https://doi.org/10.26438/ijcse/v10i7.111Keywords:
Supervised learning, classification, regression, Naïve Bayes theorem, SVM, Linear Regression, Decision Trees, coronary artery disease (CAD)Abstract
Machine Learning is now one of the thrust areas where computers are trained automatically learn from the given data automatically without any human intervention. It is the study of making machine learn automatically and do the things through algorithms which humans are doing without being explicitly programmed. Decision making is a major problem that effects the entire system under consideration irrespective of commercial databases, transactional databases, e- commerce data, social networking data or any other of that kind. Predicting the future and taking a right decision at right time is a big challenge. Supervised machine learning algorithms are solutions to those kinds of problems that are faced. They have a wide range of applications. Due to the lack of well-defined principles, choosing a suitable ML algorithm for a given problem and data is a big challenge. In this paper it is intended to do a quick and brief review of famous machine learning classification algorithms, their advantages and disadvantages, their area of application and suitable algorithm suggestion for particular type of problems. In this paper evaluation is done on supervised machine learning algorithms. Based on evaluation comparison of supervised algorithms is done.
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