Predictive Data Analysis to Identify Heart Anomalies
Sabha Samreen1, Kiran Mai Cherukuri2, Dommati Venkatsai Goud3
1Sabha samreen, Department of Computer Science and engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India.
2Dr.Kiran Mai Cherukuri, Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India.
3Dommati Venkatsai Goud, Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India
Manuscript received on 04 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 2607-2611 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2914078219/19©BEIESP | DOI: 10.35940/ijrte.B2914.078219
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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: Heart disease is a usually used word to describe diseases related to heart, when heart is not efficiently performing at is best, most of this disease is acquired because of unhealthy lifestyle and unhealthy food. Heart diseases need regular care to improve the patient’s quality of life. We can analyze cardiac disabilities of a individual by factors like historical health data and risk factors .The fusion of algorithms with clinical data can forecast the results of any disease so, incorporating these two things for Predicting and diagnosis of the heart functionality using the computational algorithms where the user interface in developed in R studio. Foremost objective of the system is for majorly predicting the heart anomalies collected using the real time clinical data .The proposed method uses the performance comparison of the algorithms and as well as the datasets like random forest and logistic regression to calculate which gives highest accuracy rate performance and this study also involves use of two different datasets, one which is available in the existing dataset for heart disease and another which was collected from the hospital in real time, so this can help in making an efficient system that can be utilized to predict the probability of heart diseases of any individual. Thus this can form a foundation for any therapy or treatment to be given this would increase the efficiency as well as help the medical staff and doctors to predict heart disease and more accurately. Computer diagnosis and prediction of a disease can solve many medical problems by predicting it beforehand.
Index Terms: Logistic Regression, Random forest, Datasets.
Scope of the Article: Data Mining