Around the globe the leading cause of cancer mortality in women is Breast Cancer; BCs, have been under many studies and several analyses have shown that many abnormal conditions and risk of BCs can be diagnosed with the help of fast and perfect judgment of the clinical physicians of this domain. This kind of intelligent decision making is not the thing where every person is good at so here, we have kept Artificial intelligence to use with supporting steps like Machine Learning Classifiers from Wisconsin Breast Cancer Dataset; WBCD. In the Combination of Human and Computer Intelligence, we have worked our way around the hybrid Mechanism where we can make the use of both entities and detect as well as prevent the risks of Very Early Stages of BCs. Our Mechanism includes the use of Principle Component Analysis; PCA, at the first stage of pre-processing the decreasing features in the pre-processed data the second stage of pre-processing was more beneficial for accessing the multi pre-processed data for analysis and diagnosis. When the Multi Pre-processed data is accessed by the Support Vector Machine; SVM, the old Accuracy, Specificity and Significance level was ranging from 95 to 100% but the new levels after the multi pre-processing were decreased to 5% and the improvement was considerable and confirmed that the proposed Algorithm has achieved the optimized results then RF, KNN, DT. It means the Proposed algorithm can assist the human factor with faster decision making and can categorize data (High Risk) malignant and (Low Risk) Benign because to avoid Human errors AI is useful and can achieve optimal results.

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Advertise bushel investigation analyzes customers' purchasing patterns by finding unexpected and important associations among the products which they place in their shopping baskets. It not only assists in decision-making process but also increases sales in many business organizations. A priori and FP Growth are the most common algorithms for mining frequent itemsets. For both algorithms a predefined minimum support is needed to satisfy for identifying the frequent itemset. But when the minimum support is low, a huge statistical change of count in candidate sets will be generated that needs large computation. In this paper, an approach has been proposed to avoid this large computation by reducing the items of dataset with top selling products. Various percentages of trending products like 30 to 55% are selected and for both FP growth and a priori algorithm, association rule generation process starts along with frequent item combinations. The comes about appear that in case beat offering things are utilized, it is conceivable to urge nearly same visit itemset and affiliation rules inside a brief time comparing with that yields which are derived by computing all the things. From time comparison, it is also found that Distributed FP Development calculation takes littler time than a priori calculation.