Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
Mrs.S.A.Shete1, Sahil Mazire2, Sarvesh Khade 3, Venkatesh Saraf 4
1Lecturer, Department of IT, AISSMS Polytechnic, Pune, Maharashtra, India
2Final Year Student, Department of IT, AISSMS Polytechnic, Pune, Maharashtra, India
3Final Year Student, Department of IT, AISSMS Polytechnic, Pune, Maharashtra, India
4Final Year Student, Department of IT, AISSMS Polytechnic, Pune, Maharashtra, India
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Abstract - Heart disease is a prevalent and life-threatening condition worldwide, necessitating accurate prediction models to aid in early diagnosis and intervention. In this paper, we present the implementation of a heart disease prediction software utilizing machine learning techniques, specifically logistic regression algorithm. Leveraging a comprehensive dataset containing various clinical and demographic features, our software employs logistic regression to predict the likelihood of an individual developing heart disease. We discuss the preprocessing steps, feature selection methods, model training, and evaluation techniques employed in the development of our predictive model. Additionally, we provide insights into the software architecture, user interface design, and deployment strategies, ensuring usability and accessibility for healthcare professionals. Through rigorous testing and validation, our software demonstrates promising performance metrics, suggesting its potential as a valuable tool in clinical settings for early detection and management of heart disease.
With heart disease being a leading cause of mortality globally, the development of accurate prediction tools is imperative for timely intervention and prevention. In this study, we present the implementation of a heart disease prediction software utilizing a machine learning approach, specifically the logistic regression algorithm. Our software harnesses a diverse dataset comprising clinical and demographic features to train and validate the predictive model. We delve into the intricacies of data preprocessing, feature selection, model training, and evaluation techniques employed to enhance predictive performance. Furthermore, we discuss the software architecture, user interface design, and deployment strategies to ensure seamless integration into clinical workflows. Through rigorous testing and validation on real-world data, our software exhibits promising results, indicating its potential as an effective tool for early detection and risk assessment of heart disease, thereby contributing to improved patient outcomes and healthcare management.
Key Words: Machine Learning, Heart Disease, Cardiovascular Health, Data Mining, Feature Extraction ,Classification, Algorithms, Risk Prediction, Signal Processing, Healthcare Technology Diagnostic Models
