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Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods.

The transfer of knowledge and skills is a process that is closely linked to the measurement of its usefulness. It's hard to imagine teaching without considering the results and evaluation of students. At the same time, the current education system is based on a longstanding traditional paradigm. The aim of this article is to propose a new digital review system based on Microsoft visual studio (MSVS); it is the only one of its kind in terms of application versatility and also features professional interfaces for the interactive display of questions and answers. In our school, we have systematically assessed this program to determine the system performance of teaching staff and students, the findings of this study demonstrate the assumption that students were prepared for this type of knowledge; the evaluation result of the propose exam system outperforms conventional system about approximately 80%.

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In recent years, the amount of data has been increased dramatically, driven by many real-world fields such as marketing, learning, social media, multimedia, medicine…etc. Because of that, data mining algorithms have extensively used on these data to serve as one of the newest data modeling and analytical tools, by which, a knowledge-rich environment can be generated and decision-making can be improved. Data mining tools can be employed for reducing these tests and predicting future trends by valuable information-driven decisions.  There are two categories of data mining algorithms: descriptive and predictive. The rules of clustering, association, summarization, and sequence discovery will be associated with descriptive type. On the other hand, predictive type will compromise classification, regression and time series analysis rules. In this paper, a study have been presented for helping specialists and physicians in Iraq to investigate heart problems via (Weka 3.8.3) software focusing on four data mining classification techniques (1BK, J48, Naïve Bayes and REPTREE). The predictive precision tests, the ROC curve, and the AUC value are calculated using a compiled dataset that have been obtained from the hospital of Ibn al-Bitar and the hospital of Baghdad medical city. The performance of the J48 technique (94.5%) indicates optimum performance based on SMO no performance factor of Baghdad medical city.