Computerization has become a necessity in recent years. As a result, large amounts of digital data have accumulated across all industries. Data mining approaches have emerged to deal with this rich data and sparse information. Data mining is a process that uses computers to extract and explore hidden patterns in data. This knowledge discovery data mining process can be understood and utilized by users. Different weights are assigned to these attributes, depending on the value associated with each property. This study takes into account the weight prescribed by doctors. The first method presented is multi-class weighted association classification with confidence-based rule ranking, while the last step presented is genetics-based rule selection with a distributed multi-class weighted association classifier. Distributed weighted association classification is used as a consequence of the results, which reduces the cost of communication, while maintaining the advantages of the centralized approach. The % accuracy of multiclass classification improves when weighted associative classification is used.