Full Text

Abstract

Heart failure is a serious cardiovascular condition that affects millions of people worldwide and poses a significant burden on healthcare systems. Early detection and prediction of heart failure can significantly improve patient outcomes by enabling timely intervention and management. In recent years, machine learning techniques have emerged as powerful tools for developing predictive models in healthcare. This abstract presents a heart failure prediction system that utilizes machine learning algorithms to identify individuals at risk of developing heart failure. The system incorporates various features such as demographic information, medical history, vital signs, and laboratory test results to build a predictive model. Data preprocessing techniques are applied to handle missing values, normalize the data, and address data imbalances. The selected machine learning algorithm undergoes training and validation using a large dataset of heart failure cases. The model\"s performance is evaluated based on accuracy, sensitivity, specificity, and area under the ROC curve. The system\"s user-friendly interface allows healthcare professionals to input patient data, view the prediction results, and make informed decisions regarding patient care. The implementation of the heart failure prediction system involves the use of modern tools and technologies such as Scikit-Learn, TensorFlow, and Keras for algorithm selection and model development. Data storage and retrieval are handled using a relational database management system such as MySQL. Privacy and ethical considerations are addressed through robust data protection measures and compliance with relevant regulations. The evaluation and results analysis demonstrate the system\"s effectiveness in predicting heart failure cases with high accuracy and sensitivity. A comparison with existing prediction systems highlights the system\"s competitive performance and its potential to enhance early detection and intervention. In conclusion, the heart failure prediction system presented in this abstract offers a valuable tool for healthcare professionals in identifying individuals at risk of heart failure. The system\"s implementation, evaluation, and comparison with existing approaches contribute to the growing body of knowledge in the field. Future work could focus on enhancing the system\"s interpretability, generalizability, and integration with real-time monitoring devices for continuous heart failure risk assessment.

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Keywords

Heart failure prediction, machine learning algorithms, data privacy, accuracy, user-friendly interface.