Integrated LSTM and PCNN Framework for Heart Disease Prediction, Treatment Recommendation, and Side Effects Management
Keywords:
Clinical Records, Data Mining. Heart Disease, Long short term memory, Pulse coupled neural network, treatment Protocol.Abstract
Heart disease remains a predominant cause of mortality globally, necessitating advanced predictive, prescriptive, and management strategies to enhance patient outcomes. This paper presents a comprehensive framework utilizing machine learning and data mining techniques for heart disease prediction, treatment recommendation, and side effects management. Firstly, we employ Long Short-Term Memory (LSTM) to expect heart disease by analyzing temporal dependencies in patient health records, achieving high accuracy through effective handling of time-series data. Secondly, we introduce a Pulse Coupled Neural Network (PCNN)-based treatment recommendation system that identifies optimal treatments by recognizing complex patterns in patient data and synchronizing neuron responses to deliver personalized therapy suggestions. Thirdly, we address side effects management by implementing standardized treatment protocols derived from extensive data mining of clinical records, which helps in mitigating adverse effects and refining therapeutic approaches. The integration of these components into a unified data mining application demonstrates significant potential in transforming heart disease care, providing a robust tool for clinicians to make informed decisions, improve patient care, and streamline healthcare processes.
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