Implementation of Regression Analysis for Forecasting Concurrent Symptoms of Cardiovascular Diseases

Implementation of Regression Analysis for Forecasting Concurrent Symptoms of Cardiovascular Diseases in the medical informatics domain, deep neural networks have lately seen a novel use in the prediction and diagnosis of cardiovascular diseases (CVDs). These models are efficient in detecting the clinical indicators, determining the disease distribution, and recommending the treatment approach for complex conditions. The objective of this study is to employ deep learning regression on a dataset of 2621 patient records from a hospital in the UAE to detect early-stage cardiovascular diseases (CVDs). More precisely, the dataset comprising data on a patient's age, health complaints, and comprehensive information on various cardiovascular diseases. Analysis of pairs of diseases significantly enhances the accuracy of the prediction due to the presence of common symptoms. Specifically, the right rate of prediction for coronary heart disease is 71%. Achieving a 10% increase to 84% is a fundamental expectation from effective management and CEO leadership. The incidence is 4% when utilised in conjunction with Dyspnoea. Furthermore, the accuracy rises to 86%, which is near the aim of 100% and so accomplishes the objective of the study. A prevalence rate of 7% was observed when the three chest disorders of Dyspnoea, Chest Pain, and Cyanosis were included. Moreover, when Weakness is concurrent with Fatigue and Empties, the diagnostic accuracy is enhanced. Emphasising the aspect, these results highlight the suitability of a certain deep learning architecture called LSTM-based neural network in improving the early prediction of cardiovascular illnesses via regression analysis. The complexity of our model in identifying common symptoms among various types of cardiovascular diseases supports the argument that deep learning models can enhance early prediction of cardiovascular diseases. This is particularly true when these models use regression analysis to separate similar symptoms. Index Terms: Artificial Neural Network (ANN), Heart Disease Analysis, Health Informatics, Long Short-Term Memory (LSTM), Predictive Analysis, Early Diagnosis, Similar Symptoms, UAE Emergency Records, Disease Simulation, Intervention Suggestions.