Classification of Pulmonary Nodules by Using Hybrid Features

Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity)

Economics of suicide in Sweden

Suicide is social tragedy that devastates families and is very costly for society. Even though suicide have been a known social problem for over a century society have yet to solve it. The purpose of this essay is to examine whether the socio-economic theory can explain the variance of suicide rate in Sweden. From previous studies and socioeconomic theories, the variables unemployment, divorce rate, fertility was picked because of their ability to explain the variance of suicides rates. Population density was also picked because of its close relation with social isolation. A two-way fixed- effect model controlling for region and time was employed on a panel of 21 counties over the years 2005-2017. The results of the regression were that all independent variables, but population density were insignificant. The study concludes that the panel employed are not enough to determine whether the socio-economic factors can explain the variance of suicide rates in Sweden.Självmord är en social tragedi som ödelägger familjer och är en stor kostnad för samhället. Även om självmord har varit ett känt problem i århundraden så är det fortfarande ett olöst problem. Syftet med den här uppsatsen är att undersöka om socioekonomisk teori kan användas för att förklara variansen av självmord i Sverige. Från tidigare studier och socioekonomiska teorier utrönandes tre variabler som anses kunna förklara variansen av självmord. De tre variablerna var arbetslöshet, skilsmässor och fertilitet. Befolkningstäthet lades till i regressionen, eftersom den ansågs vara i relaterad till sociologiska teorier. En tvåvägs fasteffekt regression som kontrollerar för län- och tid effekter applicerades på en panel bestående av 21 län under åren 2005–2017. Resultatet av regression visade att alla variabler förutom befolkningstäthet var icke signifikanta. Studien konkluderar att panelen som analyserats inte är tillräckligt för bedöma om socioekonomiska teorier kan förklara variansen av självmord

Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches

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

Bagging Support Vector Machine Approaches for Pulmonary Nodule Detection

In this paper, pulmonary nodules extracted from computed tomography (CT) images are classified by the single and bagging support vector machine (SVM) classifiers. To determine features, two dimensional principal component analysis is performed. In order to select the best features, three different models are proposed. These models are tested with classifiers of both single SVM and bagging SVM. As a result of tests, bagging SVM is shown to be superior to single SVM

Evaluation of bagging ensemble method with time-domain feature extraction for diagnosing of arrhythmia beats

We explore the effect of using bagged decision tree (BDT) as an ensemble learning method with proposed time-domain feature extraction methods on electrocardiogram (ECG) arrhythmia beat classification comparing with single decision tree (DT) classifier. RR interval is the main property which defines irregular heart rhythm, and its ratio to the previous value and difference from mean value are used as morphological feature extraction methods. Form factor, its ratio to the previous value and difference from mean value are used to express ECG waveform complexity. In addition, skewness and second-order linear predictive coding coefficients are added to the feature vector of 56,569 ECG heart beats obtained from MIT-BIH arrhythmia database as time-domain feature extraction methods. The quarter of ECG heart beat samples are used as test data for DT and BDT. The performance measures of these classifiers are evaluated using the metrics such as accuracy, sensitivity, specificity and Kappa coefficient for both classifiers, and the performance of BDT classifier is examined for number of base learners up to 75. The BDT results in more predictive performance than DT according to the performance measures. BDT with 69 base learners has 99.51 % of accuracy, 97.50 % of sensitivity, 99.80 % of specificity and 0.989 of Kappa coefficient while DT gives 98.78, 96.05, 99.57 and 0.975 %, respectively. These metrics show that the suggested BDT increases the numbers of successfully identified arrhythmia beats. Moreover, BDT with at least three base learners has higher distinguishing capability than DT

Breast Cancer Classification by Using Support Vector Machines with Reduced Dimension

Correct and timely diagnosis of diseases is an essential matter in medical field. Limited human capability and limitations decrease the rate of correct diagnosis. Machine learning algorithms such as support vector machine (SVM) can help physicians to diagnose more correctly. In this study, Wisconsin diagnostic breast cancer (WDBC) data set is used to classify tumors as benign and malignant. Independent component analysis (ICA) is used to reduce the dimensionality of WDBC data into two feature vectors. The effect of using two reduced features to classify breast cancer with SVM and polynomial or radial basis function (RBF) kernels are investigated. Performances of these classifiers are evaluated to find out accuracy, sensitivity and specificity. In addition, the receiver operating characteristics (ROC) curves of SVM with these kernels are presented. Results show that SVM with quadratic kernel provides the most accurate diagnosis results (94.40%) and decreases the accuracy and sensitivity values slightly when the dimensionality is reduced into two feature vector computing two independent components

SUCCESS OF ENSEMBLE ALGORITHMS IN CLASSIFICATION OF ELECTRICAL IMPADENCE SPECTROSCOPY BREAST TISSUE RECORDS

In this study was performed by using records from breast tissue electrical impedance spectroscopy analysis. The aim of the study is to reveal the impact of ensemble algorithms on success of the classification performance in the classification of normal and pathological breast tissue classification. For this purpose have been used three different ensemble algorithms they are bagging, adaboost, random subspaces and three main basic classifiers, which are RF, YSA, DVM. The results obtained are supplemented with performance analysis and ensemble algorithms have been demonstrated to increase classification performance results. The results obtained by the combined use of adaboost ensemble algorithm with RF basic classifier demonstrate, that the success rate was higher than the others (%89.62)

Random subspace method with class separability weighting

The random subspace method (RSM) is one of the ensemble learning algorithms widely used in pattern classification applications. RSM has the advantages of small error rate and improved noise insensitivity due to ensemble construction of the base-learners. However, randomness may cause a reduction of the final ensemble decision performance because of contributions of classifiers trained by subsets with low class separability. In this study, we present a new and improved version of the RSM by introducing a weighting factor into the combination phase. One of the class separability criteria, J3, is used as a weighting factor to improve the classification performance and eliminate the drawbacks of the standard RSM algorithm. The randomly selected subsets are quantified by computing their J3 measure to determine voting weights in the model combination phase, assigning lower voting weight to classifiers trained by subsets with poor class separability. Two models are presented including J3-weighted RSM and optimized J3 weighted RSM. In J3 weighted RSM, computed weighting values are directly multiplied by class assignment posteriors, whereas in optimized J3 weighted RSM, computed weighting values are optimized by a pattern search algorithm before multiplying by posteriors. Both models are shown to provide better error rates at lower subset dimensionality

Classification of pulmonary nodules by using hybrid features

Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity)