Table 2.
Studies show the role of AI in the diagnosis, and prediction of, DM, DFI, and CVD.
| SN | Citations | IC | DS | REL | PRE | ClassTy | TOC | ML/DL | ACC % | AUC | SEN | SPE | F1 | MCC |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Parthiban et al. [127] (2012) | LBBM | 341 | DM, CVD, and AI | CVD | SVM | NB | ML | 74.23 | 0.73 | 0.79 | NR | NR | NR |
| 2 | Jelinek et al. [128] (2016) | OBBM, LBBM | 88 | DM, CVD, and AI | CVD | SVM | RF | ML | 81.00 | 0.89 | 0.91 | 0.89 | NR | NR |
| 3 | Zarkogianni et al. [129] (2017) | OBBM, LBBM | 560 | DM, CVD, and AI | CVD | SVM | NB | ML | 76.34 | 0.87 | 0.79 | 0.76 | NR | NR |
| 4 | Basu et al. [130] (2018) | OBBM, LBBM | 2529 | DM, CVD, and AI | Death | PCA | KNN, DT | ML | 84.34 | 0.843 | 0.87 | NR | 0.76 | 0.843 |
| 5 | Dinh et al. [101] (2019) |
OBBM, LBBM | 131 | DM, CVD, and AI | DM, CVD | XGBoost | RF | ML | 84.10 | 0.81 | 0.78 | 0.73 | NR | NR |
| 6 | Segar et al. [131] (2019) | OBBM, LBBM | 319 | DM, CVD, and AI | Heart Failure | LDA | RF | ML | 76.00 | 0.778 | 0.76 | NR | 0.79 | 0.778 |
| 7 | Aggarwal et al. [116] (2020) | OBBM, LBBM | 526 | DM, CVD, and AI | CVD | SVM | ANN | ML | 86.00 | 0.863 | NR | 0.81 | 0.71 | NR |
| 8 | Derevitskii et al. [115] (2020) | OBBM, LBBM | 8139 | DM, CVD, and AI | Stroke, DM | XGBoost | NB | ML | 84.53 | 0.87 | 0.91 | 0.86 | NR | NR |
| 10 | Hossain et al. [132] (2021) |
OBBM, LBBM | 4819 | DM, CVD, and AI | CVD | SVM | RF | ML | 88.16 | 0.80 | NR | NR | 0.88 | NR |
| 11 | Longato et al. [103] (2021) |
OBBM, LBBM | 24676 | DM, CVD, and AI | CVD | SVM | CNN | DL | 79.81 | 0.76 | 0.84 | NR | 0.79 | NR |
| SN | Citations | IC | DS | REL | PRE | ClassTy | TOC | ML/DL | ACC % | AUC | SEN | SPE | F1 | MCC |
| 13 | Hyerim et al. [102] (2022) |
OBBM, LBBM | 10442 | DM, CVD, and AI | DM, CVD | LR, DT | CNN | DL | 80.88 | 0.86 | 0.81 | NR | NR | NR |
| 14 | Goyal et al. [30] (2020) | OBBM, LBBM | 7136 | DFI and AI | Diabetic foot Infection | NR | CNN | DL | 91.21 | 0.93 | 0.84 | 0.89 | NR | NR |
| 15 | Alzubaidi et al. [51] (2020) | OBBM, LBBM | 754 | DFI and AI | DFI | KNN | DNN | DL | 93.04 | 0.91 | 0.87 | 0.83 | 0.94 | NR |
| 16 | Khandekar et al. [100] (2021) | LBBM (IR) | 202 | DFI and AI | Diabetic foot | 6 Models |
CNN | DL | 92.51 | 0.92 | NR | NR | 0.81 | NR |
| 17 | Isaza et al. [29] (2021) | OBBM, LBBM | 146 | DFI, CVD, and AI | DFI | PCA | CNN | DL | 88.24 | 0.84 | 0.86 | 0.79 | NR | NR |
SN: serial number, IC: input covariates, DS: data size, REL: Relation, PRE: Prediction, ClassTy: Classifier type, OBBM: Office base biomarker, LBBM: Lab base biomarker, FE: feature extraction, TOC: Type of classifier, ACC: Percentage accuracy, SEN: Sensitivity, SPE: Specificity, MCC: Mathew coefficient correlation, AUC: Area under curve, DL: Deep learning, ML: Machine Learning, CNN: Convolution neural network, DFI: Diabetic Foot Infection, DNN: Deep neural network, RF: Random forest, SVM: Support vector machine, DT: Decision tree, LR: Logistic Regression, US: Ultrasound, NR: not reported.