UTILIZING DATA MINING AND NEURAL NETWORKS TO OPTIMIZE CLINICAL DECISION-MAKING AND PATIENT OUTCOME PREDICTIONS

Abstract

Background: Clinical decision-making and predicting patients' outcomes are essential areas of healthcare. These areas require efficient tools to analyze complex medical data for accuracy and better outcomes. Data mining and neural networks might be able to find a solution by unearthing patterns and making accurate predictions. Objectives: This integrates the techniques of data mining and neural networks to bring better clinical decisions and optimized strategies for treatment using higher predictive accuracy for better patients' outcomes for improving the health care delivery systems and well-being of patients. Methods: Applying data mining algorithms and neural networks on health care datasets will be able to find patterns and predict outcomes that help optimize decision-making processes. The hybrid approach, therefore, is the combination of both methods that enhance predictive performance and clinical efficiency. Results: From the findings presented above, it is evident that incorporation of neural networks in analysis of data mining results will improve predictive accuracy, precision, and recall in patient outcome, thereby promoting accurate and better clinical decision and treatment in health care. The present hybrids model is therefore stronger than either of the two models considered in isolation. Conclusion: The approach towards making the clinical decisions in health care for obtaining the better quality is more robust when combining data mining with neural networks.