Integrating Clinical Data and Advanced Analytics for Accurate Readmission Predictions in Heart Failure

Heart failure (HF) is a chronic and progressive condition that significantly impacts healthcare systems worldwide due to high hospitalization and readmission rates. Traditional prediction models frequently rely on clinical assessments and historical data, but they cannot provide the accuracy required for effective intervention. Integrating clinical data with advanced analytics offers a promising approach to improving readmission prediction models, enabling targeted interventions for high-risk patients. The research aimed to develop an accurate 30 days and 60 days readmission prediction model for HF patients using clinical data and deep learning (DL) techniques. An Efficient Cockroach Swarm-tuned Deep Belief Network (ECS-DBN) model is provided to predict the risk of readmission in heart failure patients. The dataset included HF clinical information in readmissions. The dataset included health records, diagnostic test results, treatment history, and patient demographics. Data cleaning and normalization are performed to ensure accuracy and consistency. Efficient cockroach swarm optimization is employed to fine-tune the hyperparameters of the DBN, enhancing its predictive accuracy and computational efficiency of readmission in heart failure patients. An ideal categorization threshold was established based on anticipated cost reductions, and performance was evaluated using the correlation statistic. The ECS-DBN model outperformed other techniques, achieving a high accuracy (0.96), recall (0.63), precision (0.97), F1-Score (0.65) and recall (0.63) compared to the conventional method in 60 days. The results show that using advanced analytics to analyze clinical data enhances the prediction of readmission in patients with HF. By identifying high-risk individuals early on, the suggested paradigm optimizes healthcare by enabling focused interventions and improving clinical outcomes.