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Real-Time Data Used for the Testing and Evaluation
| Dataset Description | Dataset Description | No. of Records | No. of Attributes | Associated Tasks | Training Data / Testing |
|---|---|---|---|---|---|
| Real-Time Datasets | 1,190 | 200 | 14 | Classification | 80:20 |
Comparative Analysis of the Different Algorithm In Handling the Public health datasets
| Algorithm | Accuracy (%) | Precision (%) | Recall (%) | Specificity (%) | F1-Score (%) |
|---|---|---|---|---|---|
| LSTM | 91.3% | 90.4% | 90.2% | 89.2% | 89.3% |
| CNN+LSTM | 91.5% | 91.34% | 91.48% | 91.35% | 91.6% |
| RNN+LSTM | 92.4% | 93.7% | 93.0% | 93.2% | 93.0% |
| HRFLM | 89.0% | 89.9% | 89.78% | 89.68% | 90% |
| RFRS | 86.4% | 87.2% | 87.4% | 87.43% | 87.2% |
| MDCNN | 95.2% | 95.2% | 94.9% | 94.5% | 94.35% |
| Proposed Model | 98.17% | 98.1% | 98.1% | 98.17% | 98.15% |
Comparative Analysis of the Different Algorithm In Handling the Framingham datasets
| Algorithm | Accuracy (%) | Precision (%) | Recall (%) | Specificity (%) | F1-Score (%) |
|---|---|---|---|---|---|
| LSTM | 91.3% | 90.4% | 90.2% | 89.2% | 89.3% |
| CNN+LSTM | 91.5% | 91.34% | 91.48% | 91.35% | 91.6% |
| RNN+LSTM | 92.4% | 93.7% | 93.0% | 93.2% | 93.0% |
| HRFLM | 89.0% | 89.9% | 89.78% | 89.68% | 90% |
| RFRS | 86.4% | 87.2% | 87.4% | 87.43% | 87.2% |
| MDCNN | 95.2% | 95.2% | 94.9% | 94.5% | 94.35% |
| Proposed Model | 98.17% | 98.1% | 98.1% | 98.17% | 98.15% |
Evaluation Metrics utilized for the assessment
| SL.NO | Evaluation Metrics | Mathematical Expression |
|---|---|---|
| 01 | Accuracy |
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| 02 | Recall |
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| 03 | Specificity |
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| 04 | Precision |
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| 05 | F1-Score |
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Comparative Assessment of Distinct Algorithm In Handling the UCI datasets
| Algorithm | Accuracy (%) | Precision (%) | Recall (%) | Specificity (%) | F1-Score (%) |
|---|---|---|---|---|---|
| LSTM | 91.3% | 90.4% | 90.2% | 89.2% | 89.3% |
| CNN+LSTM | 91.5% | 91.34% | 91.48% | 91.35% | 91.6% |
| RNN+LSTM | 92.4% | 93.7% | 93.0% | 93.2% | 93.0% |
| HRFLM | 89.0% | 89.9% | 89.78% | 89.68% | 90% |
| RFRS | 86.4% | 87.2% | 87.4% | 87.43% | 87.2% |
| MDCNN | 95.2% | 95.2% | 94.9% | 94.5% | 94.35% |
| Proposed Model | 98.17% | 98.1% | 98.1% | 98.17% | 98.15% |
Comparative Analysis of the Different Algorithm In Handling the Real time Datasets
| Algorithm | Accuracy (%) | Precision (%) | Recall (%) | Specificity (%) | F1-Score (%) |
|---|---|---|---|---|---|
| LSTM | 91.3% | 90.4% | 90.2% | 89.2% | 89.3% |
| CNN+LSTM | 91.5% | 91.34% | 91.48% | 91.35% | 91.6% |
| RNN+LSTM | 92.4% | 93.7% | 93.0% | 93.2% | 93.0% |
| HRFLM | 89.0% | 89.9% | 89.78% | 89.68% | 90% |
| RFRS | 86.4% | 87.2%% | 87.4% | 87.43% | 87.2% |
| MDCNN | 95.2% | 95.2% | 94.9% | 94.5% | 94.35% |
| Proposed Model | 98.17% | 98.1% | 98.1% | 98.17% | 98.15% |
Datasets Details Used for the Experimentation
| Dataset Description | Dataset Description | No. of Records | No. of Attributes | Associated Tasks | Training Data / Testing |
|---|---|---|---|---|---|
| UCI Machine | 18,000 | 203 | 55 | Classification | 80:20 |
| Learning Public Health | 3,300 | 1,000 | 9 | Prediction | 80:20 |
| Datasets Framingham | 3,780 | 3800 | 10 | Prediction | 80:20 |