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Table 7 Summary of different prediction models used for diabetes

From: Survey on clinical prediction models for diabetes prediction

Paper no Dataset Prediction model Technique Tool Outcome Accuracy
11 Koges Multi stage adjustment model Not mentioned Not mentioned Which person is most likely to develop diabetes Not mentioned
12 Five patients data Physiological model Svr Not mentioned Predicts blood glucose level 30 min in advance Not mentioned
17 Geriatric Hospital Sparse factor graph model Not mentioned Not mentioned Forecast diabetes complications and uncover underlying relationship between diabetes and lab reports Not mentioned
18 Pima Hybrid model to predict Clustering + C4.5 Weka Predict whether the diagnosed patient may develop diabetes within 5 years or not 92.38%
19 Pima Hybrid prediction model Clustering + SVM Weka Optimal feature subset which helps in detecting diabetes with high accuracy 98.9247%.
20 Pima Neural networks Multilayer neural network and probabilistic neural network Not mentioned Output the accurate classifier in predicting diabetes  
21 Pima Hybrid-twin support vector machine Kernel functions Not mentioned Predicts whether a new patient is suffering from diabetes or not 87.46%.
22 Jaber Abn Abu Aliz Prediction model J48 classifier Weka Classify type 2 diabetic treatment plans 70.8%.
23 Ar Hospital Logistic regression model Bipolar sigmoid function that is calculated using neuro based weight activation function Not mentioned Predicts what are different types of disease a diabetic patient can develop 90.4%
24 Not mentioned Fnc model Fuzzy logic, neural network, case based reasoning, rule based algorithm Matlab and Mycbr plug-in Used for diabetes diagnosing Not mentioned
25 Pima Ksvm Feature selection algorithm Not mentioned Used for diabetes diagnosing 99.82–50, 99.85–60, and 99.90–70% of data
26 Manual collection Cart   Manual Used to predict whether a person would develop diabetes or not 75%
27 Pima Correlation analysis Multiple regression Manual Predicts whether patient develops diabetes or not 77.85%
36 Pima CART J48 weka Predicts whether patient develops diabetes or not 78.17%
37 Manual Neural networks Memetic algorithm Not mentioned Classify and diagnose onset and progression of diabetes 93.2%
38 Pima Prediction model C4.5 and KNN Not mentioned Predicts diabetes or not 93.43%
39 Questioner Prediction model CART R Predicts whether a person fall into diabetic in future 75%
40 Manual Prediction model LASSO, ridge and elastic net regressions R Predicts glucose level accurately Not mentioned
Current work Pima Prediction model Elastic net regression R Predicts whether a person develops diabetes or not with in 6 months To be worked out