Optimization Accuracy of Diabetes Prediction using Machine Learning Algorithm
Keywords:
Diabetes Mellitus (DM), Machine learning, Early Stage, FPGA ApplicationAbstract
In this research, the main objective will to classify the data as diabetic or non-diabetic and improve the classification accuracy. It presents an automatic prediction system for diabetes mellitus through machine learning techniques by taking into account of several limitations of traditional classifiers and provides a great relationship between patient’s symptoms with diabetes diseases and the blood sugar rate. Machine learning provides a reliable and excellent support for prediction of a DM with correct case of training and testing. Diagnosis of diabetes mellitus desires great support of machine learning classifiers to detect diabetes disease in early stage, since it cannot be cured which brings great complication to our health system. This research work will consist of three phases. The first work contributed to develop a classification algorithm for prediction of DM. The second work will contribute as diabetes classification based on Extreme Learning Machine. The third work will contribute with optimization techniques for gradient boosting to obtain best output solution with higher accuracy. Optimization technique is used for searching and classifying the good diabetic data.
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