Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care
professionals in the diagnosis of heart disease. Decision Tree is one of the successful d
ata mining techniques used. However, most research has applied J4.8 Decision Tree, based on Gain Ratio and binary discretization. Gini Index
and Information Gain are two other successful types of Decision Trees that are less used in the diagnosis of heart disease. Also other discretization techniques, voting method, and reduced error pruning are known to produce
more accurate Decision Trees. This research investigates applying a range of techniques to different types of Decision Trees seeking better performance in heart disease diagnosis. A widely used benchmark data set is
used in this research. To evaluate the performance of the alternative Decision Trees the sensitivity, specificity, and accuracy are calculated. The research proposes a model that outperforms J4.8 Decision Tree and Bagging algorithm in the diagnosis of heart disease patients.
In this research, we try to show the major features of prolog which make it
a strong expressive language about writing the expert systems and the
traditional languages lacks them as Pascal language. We also provide expert
system, the purpose of it
is the Inventory Control by apply the Fixed-Order
Quantity Model, and we clearified concept of the static and dynamic data in
Prolog. Finally, we compared between the databases in prolog and some of
their quires with Access and SQL.
Expert systems are considered as one of the main applications of artificial
intelligence, which are known as knowledge based systems. And the expert
systems are computer applications which embody some non-algorithmic
expertise for solving certain types of problems. For example, the problems
which provide advice, analysis, classification, diagnostic, explanation, teaching,
or designing…etc.
