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Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were investigated. Six feature set models were constructed including different physical parameters and they fed into the ensemble classifiers as input features. As ensemble learning techniques, bagging, gradient boosting and random subspace (RSM) were used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients were classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure were also used to evaluate diagnostic performance of the proposed ensemble classification system. The classification accuracy has reached to 98.85 % by the combination of model 6 ( five BMD + five T-score values) using RSM-RF classifier. The findings of this paper suggest that the patients will be able to be warned before a bone fracture occurred, by just examining some physical parameters that can easily be measured without invasive operations. [ABSTRACT FROM AUTHOR]

In these days, there are many various diseases, whose diagnosis is very hardly. Breast cancer is one of these type diseases. In this paper, accuracy diagnosis of normal, benign, and malign breast cancer cell were found by combining mean success rates Jensen Shannon, Hellinger, and Triangle measure which connected with each other. In this article, an diagnostic method based on feature extraction Discrete Wavelet Entropy Energy (DWEE) and Jensen Shannon, Hellinger, Triangle Measure (JHT) Classifier for diagnosis of breast cancer. This diagnosis method is called as DWEE-JHT this paper. With this diagnosis method have found optimal feature subset using discrete wavelet transform feature extraction. Then these convenient features are given to Jensen Shannon, Hellinger, Triangle Measure (JHT) classifier. Then, between classifiers which are Jensen Shannon, Hellinger, and triangle distance have been validated the measures via relationships. Afterwards, breast cancer cells are classified using Jensen Shannon, Hellinger, and Triangle distance. Mean success rate of 16 feature vector with Jensen Shannon classifier is found % 97.81. Mean success rate of 16 feature vector with Hellinger classifier is found % 97.75. Mean success rate of 16 feature vector with Triangle classifier is found % 97.87. By averaging of results obtained from these 3 classifiers are found as 97.81 % average of accuracy diagnosis. [ABSTRACT FROM AUTHOR]