Machine Learning Based Target Transformation using Regressor Classification for Heart Disease Envisage
Rincy Merlin Mathew1, M. Shyamala Devi2, Shermin Shamsudheen3
1Rincy Merlin Mathew, Lecturer, Department of Computer Science, College of Science and Arts, Khamis Mushayt, King Khalid university, Abha, Asir, Saudi Arabia.
2M. Shyamala Devi, Associate Professor, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
4Shermin Shamsudheen, Lecturer, Department of Computer Science, College of Computer Science & Information Systems, Jazan University, Saudi Arabia.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1526-1531 | Volume-8 Issue-5, January 2020. | Retrieval Number: E4987018520/2020©BEIESP | DOI: 10.35940/ijrte.E4987.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the modern scenario of technological growth, the life style of an individual varies with the economic status. The world population is prone towards chronic deadly diseases due to the variety of food habits. The usages of electronic equipments have raised the population to waste their quality time towards exercise. The lack of physical activity has symptoms towards bad quality of life. With this background information, this paper concentrates on predicting the type of heart disease by applying target transformation using various machine learning regression models. This paper uses the Heart disease data set extracted from UCI Machine Learning Repository. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data segregation is done and it is preprocessed to extract the relationship and dependency of each parameters. Second, the dataset is subjected to process to identify the target distribution of classes in the dependent variable. Third, the dataset is fitted to the Ridge regressor, Huber regressor, SGD regressor and PerceptronCV regressor by applying with and without target transformation. Fourth, dataset is feature scaled and then fitted to the Ridge regressor, Huber regressor, SGD regressor and PerceptronCV regressor by applying with and without target transformation. Fifth, the performance analysis is done by analyzing the Mean Absolute Error and R2 Score. Experimental results show that, the Perceptron regressor CV has the effectiveness with the mean absolute error of 1.00 and R2 score of 0.04 for the heart disease prediction.
Keywords: Machine Learning, Regressor, Target transformation, MAE and R2 Score.
Scope of the Article: Machine Learning.