Coronary Artery Disease (CAD) takes place in the category of fatal diseases resulting in death in our country and around the world. Each year about 340 thousand patients lost their lives due to CAD in Turkey. Early diagnosis is essential to reduce risk and prolong lifetime of these patients for diseases that require long-term treatment having death risk like CAD. For this reason, classification of CAD by using medical data processing and machine learning algorithms are important in order to develop assistive or expert systems for physicians. In this study, five different machine learning algorithms were applied to estimate whether patients in the Z-Alizadeh Sani data set extracted from the UCI machine learning pool are CAD. Accuracy, precision, recall, specificity and F1 score were compared as classification performance indicators to evaluate decision tree, random forest (RF), support vector machines (SVM), nearest neighborhood (k-NN) and multi-layer sensor (MLP) methods. According to the evaluation results, the MLP method gave high classification accuracy with 90%. It also appears that RF performs relatively better than other metrics. This results, show that these classification algorithms can be use for helping healthcare systems.

Nowadays, there exists a lot of information that can be handled from business transactions and scientific data and information retrieval is simply no longer enough for decision-making. In this paper will supervised machine learning technique is applied to the mine data warehouse for Enterprise Resource Planning (ERP) of the General Electricity Company of Libya (GECOL). This technique has been applied for the first time on the data of production, transportation and distribution departments. These data are in the form of purchase and work orders of operational material strategic equipment spare parts. This technique would extract prediction rules in order to assist the decision-makers of the company to make appropriate future decisions more easily and in less time. A supervised machine learning technique has been adopted and applied for the mining data warehouse. A well-known software package for data mining which is referred to as WEKA tool was adopted throughout this work. The WEKA tool is applied to the collected data from GECOL. The conducted experiments produce prediction models in the form set of rules in order to help responsible employees make the suitable, right and accurate future decision in a simple way and inappropriate time. The collected data were preprocessed to be prepared in a suitable format to be fed to the WEKA system. A set of experiments has been conducted on those data to obtain prediction models. These models are in the form of decision rules. The produced models were evaluated in terms of accuracy and production time. It can be concluded that the obtained results are very promising and encouraging.

Liver is a needful body organ that forms an important barrier between the gastrointestinal blood, which contains large amounts of toxins, and antigens. Liver diseases contain hepatitis B and hepatitis C virus infections, alcoholic liver disease, nonalcoholic fatty liver disease and associated cirrhosis, liver failure and hepatocellular carcinoma are primary causes of death. The main purpose of this study is to investigate which attributes are important for effective diagnosis of liver disorders by performing the machine learning approach based on the combination of Stability Selection and Random Forest methods. In order to generate more accuracy, dataset was balanced by utilizing the Random Under-Sampling method. Important ones in all attributes were detected by utilizing the Stability Selection method which was performed on sub-datasets, which were obtained with 5 fold cross-validation technique. By sending these datasets to the Random Forest algorithm, the performance of the proposed approach was evaluated within the frame of accuracy and sensitive metrics. The experimental results clearly show that the Random Under-Sampling method can potentially improve the performance of the combination of Stability Selection and Random Forest methods in machine learning. And, the combination of these methods provides new perspectives for the diagnosis of this disease and other medical diseases.