Table 4 Performance comparison of the proposed method with state-of-the-art (SOTA) techniques.
From: Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50
Study | Year | Model | Accuracy (%) |
|---|---|---|---|
Sailasya et.al.,10 | 2021 | DT | 66.00 |
Devet et.al.,14 | 2022 | SVM | 68.00 |
Sailasya et.al.,10 | 2021 | RF | 73.00 |
Devet et.al.,14 | 2022 | CNN | 74.00 |
Devet et.al.,14 | 2022 | RF | 74.00 |
Sailasya et.al.,10 | 2021 | LR | 78.00 |
Tursynova et.al.,17 | 2023 | CNN | 81.00 |
Santwana et.al.,13 | 2023 | RF | 87.22 |
Santwana et.al.,8 | 2022 | RF | 87.97 |
Luis et.al.,15 | 2023 | DNN | 92.70 |
Yeo et.al.,18 | 2023 | RNN + CNN | 93.00 |
Akter et.al.,9 | 2022 | RF | 95.30 |
Saleem et.al.,12 | 2023 | GA + LSTM | 95.35 |
Gupta et.al.,19 | 2023 | DenseNet-121 | 96.00 |
Aniwat et.al.,20 | 2021 | DNN | 96.21 |
Saleem et.al.,12 | 2023 | GA + BiLSTM | 96.45 |
Current study | 2024 | CBDA-ResNet50 | 97.87 |
