Introduction:
The expansion of an actual diabetes judgement structure by the fascinating improvement of computational intellect is observed as a chief objective currently. Numerous tactics based on the artificial network and machine-learning procedures have been established and verified alongside diabetes datasets, which remained typically associated with the entities of Pima Indian derivation. Nevertheless, extraordinary accuracy up to 99-100% in forecasting the precise diabetes judgement, none of these methods has touched scientific presentation so far. Various tools such as Machine Learning (ML) and Data Mining are used for correct identification of diabetes. These tools improve the diagnosis process associated with T2DM. Diabetes mellitus type 2 (DMT2) is a major problem in several developing countries but its early diagnosis can provide enhanced treatment and can save several people life. Accordingly, we have to develop a structure that diagnoses type 2 diabetes. In this paper, a fuzzy expert system is proposed that present the Mamdani fuzzy inference structure (MFIS) to diagnose type 2 diabetes meritoriously. For necessary evaluation of the proposed structure, a proportional revision has been originated, that provide the anticipated structure with Machine Learning algorithms, specifically J48 Decision-tree (DT), multilayer perceptron (MLP), support-vector-machine (SVM), and Naïve- Bayes (NB), fusion and mixed fusion-based methods. The advanced fuzzy expert system (FES) and the machine learning algorithms are authenticated with actual data commencing the UCI machine learning datasets. Furthermore, the concert of the fuzzy expert structure is appraised by equating it to connected work that used the MFIS to detect the occurrence of type 2 diabetes.
Objective:
This survey paper presents a review of recent advances in the area of machine learning based classification models for diagnosis of diabetes.
Methods:
This paper presents an extensive work done in the field of machine learning based classification models for diagnosis of type 2 diabetes where modified fusion of machine learning methods are compared to the basic models i.e. Radial basis function, K-nearest neighbor, support vector machine, J48, logistic regression, classification and regression tress etc. based on training and testing.
Results:
Fig. 3 and Fig. 4 summarizes the result based on prediction accurateness for each classifier of training and testing.
Conclusion:
The fuzzy expert system is the best among its rival classifiers; SVM performs very poorly with a very low true positive rate, i.e. a very high number of positive cases misclassified as (Non-diabetic) negative. Based on the evaluation it is clear that the fuzzy expert system has the highest precision value. However, J48 is the least accurate classifier. It has the highest number of false positives relative to the other classifiers mentioned in the testing part. The results show that the fuzzy expert system has the uppermost cost for both precision and recall. Thus, it has the uppermost value for F-measure in the training and testing datasets. J48 is considered the second-best classifier for the training dataset, whereas Naïve Bayes comes in the second rank in the testing dataset.
Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices
