AN Enhanced Model for Diabetes Prediction using Improved Firefly Feature Selection and Hybrid Random Forest Algorithm
B. Senthil Kumar1, R. Gunavathi2

1B. Senthil Kumar*, Assistant Professor, Department of Computer Science, Sree Narayana Guru College, Coimbatore, (Tamil Nadu), India.
2Dr. R. Gunavathi, Associate Professor & Head, Department of MCA, Sree Saraswathi Thyagaraja College, Coimbatore, (Tamil Nadu), India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 3765-3769  | Volume-9 Issue-1, October 2019 | Retrieval Number: A9818109119/2019©BEIESP | DOI: 10.35940/ijeat.A9818.109119
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Abstract: Diabetes is a chronic disease that causes numerous amount of death each year. Untreated diabetes disturbs the proper functionality of other organs in human body. Hence early detection is a significant process to have a healthy life style. Usually the performance of the classification is affected due to the existence of high dimensionality in medical data. In this study a system model is proposed on Pima dataset to enhance the classification accuracy by eliminating the irrelevant features. Therefore it is important to choose a suitable feature selection approach that provides the better accuracy in disease prediction compared to prior study. Hencenovel techniques Improved Firefly(IFF)and hybrid Random forest algorithmis proposed for feature selection and classification. The present study provides a better result with 96.3% accuracy. The efficiency of the present studyis compared with the prior classification approaches.
Keywords: Diabetics prediction, Pima dataset, Feature selection, Firefly optimization, Accuracy.