Performance Analysis of Supervised Machine Learning Algorithms on Medical Dataset
Amit Juyal1, Chetan Pandey2, Janmejay Pant3, Ankur Dumka4, Vikas Tomar5

1Amit Juyal*, School of Computing, Graphic Era Hill University, Dehradun, India.
2Chetan Pandey, School of Computing, Graphic Era Hill University, Dehradun, India.
3Janmejay Pant, Dept. of Computer Science, Graphic Era Hill University, Bhimtal, India.
4Dr. Ankur Dumka, Dept. of CSE, Graphic Era Deemed to be University, Dehradun, India.
5Vikas Tomer, Dept. of CSE, Graphic Era Deemed to be University, Dehradun, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1637-1642 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7908038620/2020©BEIESP | DOI: 10.35940/ijrte.F7908.038620

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Abstract: Machine learning (ML) algorithms are designed to perform prediction based on features. With the help of machine learning, system can automatically learn and improve by experience. Machine learning comes under Artificial intelligence. Machine learning is broadly categorized in two types: supervised and unsupervised. Supervised ML performs classification and unsupervised is for clustering. In present scenario, machine learning is used in various areas. It can be used for biometric recognition, hand writing recognition, medical diagnosis etc. In medical field, machine learning plays an important role in identifying diseases based on patient’s features. Presently, doctors use software application based on machine learning algorithm in various disease diagnosis like cancer, cardiac arrest and many more. In this paper we used an ensemble learning method to predict heart problem. Our study described the performance of ML algorithms by comparing various evaluating parameters such as F-measure, Recall, ROC, precision and accuracy. The study done with various combination ML classifiers such as, Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF) algorithm to predict heart problem. The result showed that by combining two ML algorithm, DT with NB, 81.1% accuracy was achieved. Simultaneously, the models like Support Vector machine (SVM), Decision tree, Naïve Bayes, Random Forest models were also trained and tested individually.
Keywords: Cardiovascular Disease, Ensemble Learning, Machine Learning. Naïve Bayes, Decision Tree.
Scope of the Article: Machine Learning.