Driving daily through traffic congestion has been recognised as a major cause of stress. High levels of stress while driving negatively impact the driver\'s decisions which could potentially lead to accidents and other long-term health hazards. Accordingly, there is a great need to determine stress levels for drivers based on measuring and predicting the major causes (features or classes) that increase stress levels. In this paper, the problem of predicting automobile drivers\' stress levels, as experienced during actual driving, is investigated through the application of five different data mining algorithms, namely K-Nearest Neighbour (KNN), Decision Tree (J48), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). An experiment was conducted on 14 drivers taking various routes in Amman - Jordan, with a wearable biomedical device attached to the driver to instantly collect physiological data. The collected data (dataset) is grouped into two different categories, namely \'Yes\' to signify the presence of stress and \'No\' to signify the absence of stress. In order to efficiently apply data mining algorithms to the data set, oversampling was used to avoid the negative effect of driver samples with a lesser class on the prediction of stress. The findings are evaluated in relation to stress prediction and accordingly contrasted alongside standard reference approaches that do not consider oversampling and/or feature selection using the Friedman rank test. The proposed approach, in combination with RF, was seen to surpass any others in terms of accuracy, AUC, specificity, and sensitivity. The accuracy, AUC, specificity, and sensitivity rates produced by RF utilising our proposed approach were 98.92%, 99.91%, 98.46%, and 99.36%, respectively.

Thalassemia is considered one of the most common genetic blood disorders that has received excessive attention in the medical research fields worldwide. Under this context, one of the greatest challenges for healthcare professionals is to correctly differentiate normal individuals from asymptomatic thalassemia carriers. Usually, thalassemia diagnosis is based on certain measurable characteristic changes to blood cell counts and related indices. These characteristic changes can be derived easily when performing a complete blood count test (CBC) using a special fully automated blood analyzer or counter. However, the reliability of the CBC test alone is questionable with possible candidate characteristics that could be seen in other disorders, leading to misdiagnosis of thalassemia. Therefore, other costly and time-consuming tests should be performed that may cause serious consequences due to the delay in the correct diagnosis. To help overcoming these challenging diagnostic issues, this work presents a new novel dataset collected from Palestine Avenir Foundation for persons tested for thalassemia. We aim to compile a gold standard dataset for thalassemia and make it available for researchers in this field. Moreover, we use this dataset to predict the specific type of thalassemia known as beta thalassemia (β-thalassemia) based on hybrid data mining model. The proposed model consists of two main steps. First, to overcome the problem of the highly imbalanced class distribution in the dataset, a balancing technique called SMOTE is proposed and applied to handle this problem. In the second step, four classification models, namely k-nearest neighbors (k-NN), naïve Bayesian (NB), decision tree (DT) and the multilayer perceptron (MLP) neural network are used to differentiate between normal persons and those patients carrying β-thalassemia. Different evaluation metrics are used to assess the performance of the proposed model. The experimental results show that the SMOTE oversampling method can effectively improve the identification ratio of β-thalassemia carriers in a highly imbalanced class distribution. The results reveal also that the NB classifier achieved the best performance in differentiating between normal and β-thalassemia carriers at oversampling SMOTE ratio of 400%. This combination shows a specificity of 99.47% and a sensitivity of 98.81%.

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets.