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 30Â 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 | Â |
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 | Â | 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 6Â months | To be worked out |