Performance Improvement with Combining Multiple Approaches to Diagnosis of Thyroid Cancer

There are a lot of diseases that carry death risk when these diseases are infected to human body, if early measures are not taken. Thyroid cancer is one of them. In USA, number of thyroid cancer cases resulted in death in only 2013 shows necessity of early fight with this disease. This study aims performance improvement in diagnosis of thyroid cancer with machine learning techniques. Study consists of 3 phases. In the first phase, BayesNet, NaiveBayes, SMO, Ibk and Random Forest classifiers have been trained with thyroid cancer train dataset. In the second phase, trained classifiers have been tested with thyroid cancer test dataset and the obtained performance results have been compared. In the third and last phase, approaches named above have been integrated to algorithm AdaboostMI to show difference between of ensemble classifiers from conventional individual classifiers and first two phases have been repeated. With using ensemble approaches performance improvement has been achieved in diagnosis of thyroid cancer. Also, kappa, accuracy and MCC values obtained from these classifier models have been explained in tables and effects on diagnosis of the disease have been shown with ROC graphics. All of these operations have been carried out with WEKA data mining program.

Sleep Apnea Detection Using Adaptive Neuro Fuzzy Inference System

This paper presents an efficient and easy implemented method for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal based respiratory signals extracted from polysomnography recordings are obtained from PhysioNet apnea-ECG database. Wavelet transforms are applied on the 1-minute and 3-minute length recordings. According to the preliminary tests, the variances of 10th and 11th detail components can be used as discriminative features for apneas. The features obtained from total 8 recordings are used for training and testing of an adaptive neuro fuzzy inference system (ANFIS). Training and testing process have been repeated by using the randomly obtained five different sequences of whole data for generalization of the ANFIS. According to results, ANFIS based classification has sufficient accuracy for apnea detection considering of each type of respiratory. However, the best result is obtained by analyzing the 3-minute length nasal based respiratory signal. In this study, classification accuracies have been obtained greater than 95.2% for each of the five sequences of entire data.