Clinical Healthcare Applications: Efficient Techniques for Heart Failure Prediction Using Novel Ensemble Model

سال انتشار: 1403
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 33

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شناسه ملی سند علمی:

JR_JITM-16-1_009

تاریخ نمایه سازی: 28 اسفند 1402

چکیده مقاله:

Heart failure is a severe medical ailment that significantly impacts patients’ well-being and the healthcare system. For improved results, early detection and immediate treatment are essential. This work aims to develop and evaluate predictive models by applying sophisticated ensemble learning techniques. In order to forecast heart failure, we used a clinical dataset from Kaggle. We used the well-known ensemble techniques of bagging and random forest (RF) to create our models. With a predicted accuracy of ۸۲.۷۴%, the RF technique, renowned for its versatility and capacity to handle complex data linkages, fared well. The bagging technique, which employs several models and bootstrapped samples, also demonstrated a noteworthy accuracy of ۸۳.۹۸%. The proposed model achieved an accuracy of ۹۰.۵۴%. These results emphasize the value of group learning in predicting cardiac failure. The area under the ROC curve (AUC) was another metric to assess the model’s discriminative ability, and our model achieved ۹۴% AUC. This study dramatically improves the prognostic modeling for heart failure. The findings have extensive implications for clinical practice and healthcare systems and offer a valuable tool for early detection and intervention in cases of heart failure.

نویسندگان

Poojari

Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India.

Uppala

Department of IT, Institute of Aeronautical Engineering, Dundigal, Hyderabad.

Devi

Department of CSE, Annamacharya Institute of Technology and Sciences, Tirupati

Sujatha

Department of Electronics and Communication, Sri Venkateswara College of Engineering Karakambadi road, Tirupati, Andrapradesh, India.

Madhavi

School of Computing, Mohan Babu University, Tirupati, A.P., India.

Kumar

Department of CSE, Aditya Institute of Technology and Management, Tekkali, A.P.

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