Optimization Accuracy for Diabetes Diagnosis and Prediction using Machine Learning Technique
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Abstract
The goal of this research is to improve the overall disease prediction accuracy by analyzing the automatic prediction and recommendation of diabetes disease from the electronic health record diabetes dataset. Diabetes data is acquired from patients and are processed utilizing optimal artificial intelligence techniques during the diabetes data recognition process. This research integrated machine learning based approaches to predict diabetes disease features such as: SVM, DT, RF, LR, K-NN, NB and GB. The proposed GB model is proposed to apply diabetes diagnosis which is single class and multiclass classification problems. In the future, we shall incorporate an auto feature selection method to design the crossed features and select the features for the prediction and classification model. Subsequently, the Inclination helping calculation gives the best exactness to Diabetes finding than the past calculation.
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