Prediction of Heart Disease Using Hybrid Model |
Author(s): |
| Deepak Kakade , Zeal College of Engineering and Research; Sukrut Mane, Zeal College of Engineering and Research; Aishwarya Patodekar, Zeal College of Engineering and Research; Apurva Khurud, Zeal College of Engineering and Research; Rahul Bhole, Zeal College of Engineering and Research |
Keywords: |
| Heart Condition, Decision Tree, Random, Cleveland Database, Forest Model |
Abstract |
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Heart disease is one among the foremost significant causes of mortality within the world today. Prediction of disorder may be a critical challenge within the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the huge quantity of data produced by the healthcare industry. We’ve also seen ML techniques getting used in recent developments in several areas of the web of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. Within the proposed work, we propose a completely unique method that aims at optimized model leading to improving the accuracy within the prediction of disorder. We produce an optimized model with hybrid model combining two machine learning algorithms. |
Other Details |
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Paper ID: IJSRDV8I120040 Published in: Volume : 8, Issue : 12 Publication Date: 01/03/2021 Page(s): 53-54 |
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