Enhancing Predictive Accuracy Through Hybrid Data Mining and Advanced Analytics Techniques: A Study Across Healthcare and Financial Sectors

Abstract

In the age of big data, increased reliance on powerful analytical techniques is observed to gain insights and use of data-driven decisions by organizations. The proposed research focuses on the combination of data mining tools and powerful data analysis to raise the precision in predicting and decision-making. The study put the emphasis on two areas that are crucial to society, healthcare and finance, and uses a vintage strategy of integrating common big data analysis tools like classification, clustering, and association rule mining with machine learning models and real-time data analysis methods. The research assesses the viability of the use of hybrid models in revealing hidden patterns, predicting results and minimizing uncertainty by analyzing large amounts of data pertaining to both fields. The paper also contrasts using different approaches facing measures of performance like accuracy, precision, recall and computational effectiveness. It is anticipated that the outcomes will provide a firm foundation to using hybrid data approaches to achieve better service and operational results, risk analyses and high-sentience environments.