The article presents CardioAI-DSS for cardiovascular disease risk assessment. The system applies machine learning and CNN models to clinical data, Cardiovascular Disease datasets, and ECG signals. The models were evaluated using Accuracy, Precision, Recall, F1-score, AUC, confusion matrix, and SHAP explanations. The software supports preliminary risk assessment and does not replace physician diagnosis.
| Mualliflar | Saytov, Kamiljan Begdullaevich, Bekniyazova, Nurjamal Danaevna, Xudaynazarova, Malika Orinbaevna, Сайтов, Камилжан Бегдуллаевич, Бекниязова, Нуржамал Данаевна, Худайназарова, Малика Оринбаевна, Saytov, Kamiljan Begdullaevich, Bekniyazova, Nurjamal Danaevna, Xudaynazarova, Malika Orinbaevna |
|---|---|
| Jurnal | Ilim ha’m ja’miyet |
| Nashr sanasi | 2026-08-15 |
| Son | 5-1 |
| Betlar | 27-29 |
| Til | uz_Latn |
sun’iy intellekt, yurak-qon tomir kasalliklari, qaror qabul qilishni qo‘llab-quvvatlash tizimi, CNN, ECG, SHAP, mashinali o‘qitish, искусственный интеллект, сердечно-сосудистые заболевания, система поддержки принятия решений, CNN, ECG, SHAP, машинное обучение, artificial intelligence, cardiovascular diseases, decision support system, CNN, ECG, SHAP, machine learning
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