The extraction of healthcare data is essential for precisely forecasting and assessing critical illnesses using historical records. Various tools and technologies are employed in the practice of healthcare data mining. Technology is dependent on the utilization of data mining algorithms. Data mining provides a range of algorithms that strive to extract valuable information. Data mining techniques involve a range of methods, including rule mining, clustering, classification, and regression, to analyze large datasets. Data mining is an essential component of pattern analysis. The hybrid rule mining algorithm computes the correlation coefficient for the relative attribute during the mining process. Clustering and classification techniques play a vital role in health care databases. Clustering entails the repetitive procedure of identifying and categorizing patterns that exhibit a relationship with one another. A novel methodology has been adopted, substituting the categorization method with a directed grouping approach that depends on specific guidance. This paper examines the process of analyzing health care data patterns using different classification techniques that rely on rule mining approaches.