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Prostate cancer (PCa) is the second most diagnosed cancer of men all over the world. The aim of this research was to improve PCa detection based on image enhancement methods including image adjustment and morphological erosion operations and then compute the texture features. We then employed robust Machine Learning (ML) techniques such as the Naïve Bayes, Support Vector Machine (SVM) kernels: Polynomial, Radial Base Function (RBF), Gaussian and Decision Tree (DT) based on extracted texture features. The Cross validation (Jack-Knife k-Fold) was performed, and performance was evaluated in term of Receiver Operating Curve (ROC), Specificity, Sensitivity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR). The highest detection performance based on morphological erosion operation on texture features was obtained using SVM polynomial with sensitivity (99.82%), specificity (96.63%), accuracy (98.59%) and AUC (0.9994). The image adjustment methods yielded the highest detection performance with sensitivity, specificity, and accuracy of 100% and AUC of 1.00 using ML SVM selected kernels. The results reveal that proposed image enhancement methods have the potential to accurately predict PCa. Thus, this approach can be better utilized by clinicians for early prediction of PCa for further diagnostic and treatment of the patients. [ABSTRACT FROM AUTHOR]

In the whole world, approximately two billion people are using smartphones for their better life The smartphone's sensor data consist of high dimensions which increase complexity of training model and degrade overall performance of a classifier. We investigated and analyzed the performance of different machine learning algorithms with various dimensionality reduction techniques on publicly available smartphone-based recognition of human activities and postural transitions dataset. The highest accuracy was obtained at set 1 using support vector machine (SVM) linear kernel (97.40%) followed by set 3 using eXtreme boosting (XGBoost) (96.92%), set 2 using adaptive boosting multi-class (AdaBoost.M1) (95.69%), set 4 using AdaBoost. M1 (82.17%). Using different dimensionality reduction methods for set 1, principal component analysis (PCA) provided the highest accuracy using average neural network (AvNNET) (98.27%), while chi-squared gives the accuracy of 93.72% using the XGBoost classifier. For set 2, PCA gives the highest accuracy of 95.83% using the AvNNET classifier while for set 3 Chi-squared gives an accuracy of 84.46% using average neural network (AvNNET), SVM.L, and stochastic Gradient Boosting Machine (GBM) classifiers. The results reveal that overall dimensionality reduction methods outer performed. The outcomes will be helpful in making wise decisions in detecting various activities in anti-terrorists, surveillance, etc. [ABSTRACT FROM AUTHOR]

In the present study, based on the multivariate nature of images from lung cancer, we extracted autoencoder, reconstruction independent component analysis (RICA) and sparse filters features along with traditional texture and morphological features. The machine learning techniques such as Support Vector Machine (SVM) with Gaussian, radial base function (RBF) and polynomial kernels, decision tree and Naïve Bayes were used classification purpose. The Jackknife 10-fold cross validation (CV) was used for training/testing data formulation and performance was evaluated in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), total accuracy (TA), false positive rate (FPR) and area under the receiving operating curve (AUC) to distinguish the small cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC) images. The highest detection performance was obtained by extracting RICA and Sparse filters with 100% of sensitivity, specificity, PPV, NPV and TA using SVM with RBF, polynomial kernels and Naïve Bayes followed by Sparse filter with decision tree with TA (99.78%). The highest AUC of 1.00 was obtained by extracting RICA and Sparse filters using decision tree, SVM Gaussian, RBF and polynomial and Naïve Bayes. The results showed the good potential of these methods for lung cancer detection (LCD). [ABSTRACT FROM AUTHOR]

The incidence of congestive heart failure (CHF) is approximately 10 per 1000 for Americans over the age of 65 years. The dynamics of CHF are highly complex, nonlinear, and temporal dynamics. Based on these characteristics, we extracted multimodal features from congestive heart failure (CHF) and normal sinus rhythm (NSR) signals. We performed the synthetic minority over-sampling technique (SMOTE) to increase the number of CHF subjects to balance our train data. The classification between these subjects with original data and SMOTE data was performed using machine learning classifiers such as classification and regression tree (CART), support vector machine linear (SVM-L), Naïve Bayes, neural network, and ensemble classifiers such as random forest (RF), XG boost, averaged neural network (AVNNET). With the original data, the highest performance was obtained using SVM-L with accuracy (94.28%), sensitivity (84.61%), specificity (100%), p-value (0.0002), AUC (0.9605) with 95% CI: 0.9006-1.00. By applying the SMOET, the highest performance was obtained with SVM-L with accuracy (97.14%), sensitivity (92.30%), specificity (100%), p-value (7.99e-06), AUC (0.9650) with 95% CI: 0.8945–1.00. The results reveal that proposed approach with SMOTE improved the detection performance which can be very effective and computationally efficient tool for automatic detection of congestive heart failure patients. [ABSTRACT FROM AUTHOR]

Sleep is regulated autonomously with circadian behavior. The sleep disorders greatly impact major sleep disturbances in patients suffered from Parkinson's disease (PD) and epilepsy affecting sleep at night and increased abnormality of muscles tones during NREM stage. The aim of this research is to quantify the dynamics of different sleep pathologies by applying threshold- dependent symbolic entropy. The threshold-dependent symbolic entropy is applied to distinguish the healthy subjects with sleep disorders such as narcolepsy, sleep behavior disorder (RBD), sleep disordered breathing (SDB), period leg movement (PLM), and nocturnal frontal lobe epilepsy (NFLE) subjects. At certain smaller threshold, the healthy and narcolepsy subjects exbibit higher NCSE values than other sleep disorders showing these subjects are more complex followed by SBD, NFLE, PLM and RDB respectively. To distinguish the healthy subjects from narcolepsy, the highest separation was obtained at threshold 550ms with P-value (3.69e-04), NFLE at threshold 550ms with P-value (2.36e-12), PLM at threshold 550ms with Pvalue (2.24e-06), RBD at threshold 550ms with P-value (8.87e-09) and SDB at threshold 550ms with P-value (0.0012). Likewise, the highest AUC was obtained as AUC=0.9688 to distinguish healthy subjects from NFLE, narcolepsy, PLM and SDB followed by RBD with AUC (0.8438). [ABSTRACT FROM AUTHOR]

Cardiac arrhythmias are disturbances in the rhythm of the heart manifested by irregularity or by abnormally fast rates ('tachycardia') or abnormally slow rates ('bradycardias'). In the past researchers extracted different features extracting strategies to detect the arrhythmia. Since, signals acquired from subjects suffered with arrhythmia are multivariate, highly nonlinear, nonstationary, time variant and very complex. Traditional features extraction approaches could not capture full dynamics to detect these signals. In this study, we extracted hybrid features based on refined sample entropy (SampEn) and Fuzzy entropy (FuzEn) computed on mean, standard deviation and variance in combination with traditional features. The machine learning support vector machines with its kernels, decision tree, KNN and ensemble methods were employed to detect the arrhythmia. The 10-fold cross-validation was used for training/validation of data. The Support Vector Machine and its kernels, Fine KNN, and Ensemble Boosted Tree give the highest performance with an overall accuracy of 100%, AUC of 1.00. An accuracy of 95.5% and 100% was obtained using SVM coarse Gaussian, 97% and 100% using Fine KNN, 95.5% and 97% using Decision tree with traditional and hybrid features respectively. The results revealed that SVM coarse Gaussian, Coarse KNN, Decision tree methods outer performed using hybrid features than the traditional feature extracting strategy. [ABSTRACT FROM AUTHOR]