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This study develops a logistic model tree based automation system based on for accurate recognition of types of vertebral column pathologies. Six biomechanical measures are used for this purpose: pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis. A two-phase classification model is employed in which the first step is preprocessing the data by use of Synthetic Minority Over-sampling Technique (SMOTE), and the second one is feeding the classifier Logistic Model Tree (LMT) with the preprocessed data. We have achieved an accuracy of 89.73 %, and 0.964 Area Under Curve (AUC) in computer based automatic detection of the pathology. This was validated via a 10-fold-cross-validation experiment conducted on clinical records of 310 patients. The study also presents a comparative analysis of the vertebral column data with the use of several machine learning algorithms.

Coronary Artery Disease is a common heart disease related to disorders effecting the heart and blood vessels. Since the disease is one of the leading causes of heart attacks and thus deaths, diagnosis of the disease in its early stages or in cases when patients do not show many of the symptoms yet has considerable importance. In the literature, studies based on computational methods have been proposed to diagnose the disease with readily available and easily collected patient data, and among these studies, the greatest accuracy reached is 89.01%. This paper presents a computational tool based on the Rotation Forest algorithm to effectively diagnose Coronary Artery Disease in order to support clinical decision-making processes. The proposed method utilizes Artificial Neural Networks with the Levenberg-Marquardt back propagation algorithm as base classifiers of the Rotation Forest ensemble method. In this scheme, 91.2% accuracy in diagnosing the disease is accomplished, which is, to the best of our knowledge, the best performance among the computational methods from the literature that use the same data. This paper also presents a comparison of the proposed method with some other classifiers in terms of diagnosis performance of Coronary Artery Disease.

Cardiotocography is one of the most widely used technique for recording changes in fetal heart rate (FHR) and uterine contractions. Assessing cardiotocography is crucial in that it leads to identifying fetuses which suffer from lack of oxygen, i.e. hypoxia. This situation is defined as fetal distress and requires fetal intervention in order to prevent fetus death or other neurological disease caused by hypoxia. In this study a computer-based approach for analyzing cardiotocogram including diagnostic features for discriminating a pathologic fetus. In order to achieve this aim adaptive boosting ensemble of decision trees and various other machine learning algorithms are employed.