Improvement in prediction rate and accuracy of diabetic diagnosis system using fuzzy logic hybrid combination

Abstract

Nowadays diabetes mellitus has become the major health problem among the people of all ages. The main problem in this type of dieses is its prediction. It is found that if diabetes mellitus is detected at early stages then it can be cured. So early detection of diabetes mellitus is important. There are different techniques with the help of which early detection of diabetes mellitus is possible. In this paper combination of three different methods used for early detection of diabetes mellitus are given. These three methods are fuzzy system, neural network, case based reasoning. By using combination of all these approaches, it is found that detection of diabetes mellitus at early stages is possible. Benefit of using these systems is that accuracy of prediction rate is higher as compared to other techniques.

AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes

“…Also shows the accuracy information of Naive Bayes classifier and K-means algorithm. 80% accuracy is obtained from this method [12]. Modified extreme learning machine and back-propagation neural network method are addressed in prediction of diabetes mellitus [13].…”

“…The artificial intelligent-based methods are very effective and popular one in recent years [24]. The diagnosis of diabetes and cancer prediction the adaptive neuro-fuzzy inference system shows better accuracy [12]. Also shows the accuracy information of Naive Bayes classifier and K-means algorithm.…”

“…The input features are fed to the neurons in the input layer. AI-based smart prediction of clinical disease using random… SVM is the supervised learning model, which can perform well even with the smaller data samples [12,13]. SVM classifier has no curse of dimensionality since it has the ability to manage sparse data in high-dimensional datasets [14].…”

“…Also, SVM classifier has better generalization than the ANN and avoid local minima problem. The logic behind the SVM method is it creates the optimal separation plane under linearly separable condition [12]. The hyper plane is optimized by increasing the margin.…”