Early prediction of diabetes using Feature Transformation and hybrid Random Forest Algorithm
B. Senthil Kumar1, R. Gunavathi2
1B. Senthil Kumar*, Assistant Professor, Department of CA & IT, Sree Narayana Guru College, Coimbatore, (Tamil Nadu), India.
2Dr. R. Gunavathi, Associate Professor and Head, Department of MCA, Sree Saraswathi Thyagaraja College, Pollachi (Tamil Nadu), India
Manuscript received on May 30, 2020. | Revised Manuscript received on June 08, 2020. | Manuscript published on June 30, 2020. | PP: 787-792 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9836069520/2020©BEIESP | DOI: 10.35940/ijeat.E9836.069520
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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: Diabetes is the most common chronic disease among the world. Early prediction of these will assist the physicians to provide the improved treatment. Machine learning approaches are widely used for predicting the disease at the earlier stage. However the selecting the significant features and the suitable classifier are still reduces the diagnosis accuracy. In this paper the PCA based feature transformation and the hybrid random forest classifier is utilized for diabetes prediction. PCA attempt to identify the best subset of transformed components that greatly improves the classification result. The system is compared with priori machine learning approaches to evaluate the efficiency of this work. The experimental result shows that the present study enhances the prediction accuracy.
Keywords: Diabetes prediction, PCA, Random forest, machine learning, feature transformation