CARDIOAI-DSS DASTURIY TIZIMIDA YURAK-QON TOMIR KASALLIKLARI XAVFINI SUN’IY INTELLEKT ASOSIDA BAHOLASh

Mualliflar

  • Saytov Kamiljan Begdullaevich

    Nukus davlat texnika universiteti

  • Bekniyazova Nurjamal Danaevna

    Nukus davlat texnika universiteti

  • Xudaynazarova Malika Orinbaevna

    Nukus davlat texnika universiteti

Калит сўзлар: sun’iy intellekt, yurak-qon tomir kasalliklari, qaror qabul qilishni qo‘llab-quvvatlash tizimi, CNN, ECG, SHAP, mashinali o‘qitish

Аннотация

Maqolada yurak-qon tomir kasalliklari xavfini baholashga mo‘ljallangan CardioAI-DSS dasturiy tizimi bayon qilinadi. Tizim klinik ma’lumotlar, Cardiovascular Disease datasetlari va ECG signallarini tahlil qilish uchun klassik mashinali o‘qitish hamda CNN modellaridan foydalanadi. Natijalar Accuracy, Precision, Recall, F1-score, AUC, confusion matrix va SHAP izohi orqali baholandi. Dastur shifokor tashxisini almashtirmaydi, balki dastlabki riskni baholashda yordamchi vosita sifatida qaraladi.

Фойдаланилган адабиётлар

1. Detrano R., Janosi A., Steinbrunn W. et al. International application of a new probability algorithm for the diagnosis of coronary artery disease. // American Journal of Cardiology, Vol. 64, No. 5, 1989. -P. 304-310.

2. Moody G.B., Mark R.G. The impact of the MIT-BIH Arrhythmia Database. // IEEE Engineering in Medicine and Biology Magazine, Vol. 20, No. 3, 2001. -P. 45-50.

3. Bousseljot R., Kreiseler D., Schnabel A. Nutzung der EKG-Signaldatenbank CARDIODAT der PTB. // Biomedizinische Technik, Vol. 40, 1995. -P. 317-318.

4. Pedregosa F., Varoquaux G., Gramfort A. et al. Scikit-learn: Machine Learning in Python. // Journal of Machine Learning Research, Vol. 12, 2011. -P. 2825-2830.

5. Chen T., Guestrin C. XGBoost: A Scalable Tree Boosting System. // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.

6. Lundberg S.M., Lee S.I. A Unified Approach to Interpreting Model Predictions. // Advances in Neural Information Processing Systems, 2017.

7. Chollet F. Deep Learning with Python. - New York: Manning Publications, 2018.