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Breast cancer was the most diagnosed form of cancer in 2020. Early diagnosis of breast cancer results in a significant improvement in long-term survival rates. Current methods require consultation with experts, which is expensive and time-consuming and thus may not be accessible to all. This paper seeks to train and evaluate supervised machine learning models for the accurate and efficient detection of breast cancer. The Wisconsin Breast Cancer Database dataset describes 30 attributes of cell nuclei, including, but not limited to, their radius, texture, and concavity. It contains 569 instances, 212 of which are malignant tumors. The Random Forest algorithm outperforms other algorithms in classifying breast tumors as either malignant or benign and is thus selected as our primary model. It is trained on two different subsets of the dataset having 16 and 8 features, respectively, identified with the help of multiple feature selection methods. The Random Forest models are tested post hyperparameter tuning on a holdout set, and accuracies of 100% and 99.30% respectively. The models are also compared with four other machine learning classification algorithms: Support Vector Machine (SVM), Decision Tree, Multilayer Perceptron, and K-Nearest Neighbors. The results confirm that Random Forest is the superior method for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Currently, the smartphone devices have become an essential part of our daily activities. Smartphone' users run various essential applications (such as banking and e-health Apps), which contains very confidential information (e.g., credit card number and its PIN). Typically, the smartphone's user authentication is achieved using mechanisms (password or security pattern) to verify the user identity. Although these mechanisms are cheap, simple, and quick enough for frequent logins, they are vulnerable to attacks such as shoulder surfing or smudge attack. This problem could be addressed by authenticating the users using their behaviour (i.e., touch behaviour) while using their smartphones. Such behaviours include finger's pressure, size, and pressure time while tapping keys. Selecting features (from these behaviours) could play an important role in the authentication process's performance. This paper aims to propose an efficient authentication method providing an implicit authentication for smartphone users while not imposing an additional cost of special hardware and addressing the limited smartphone capabilities. We first investigated feature selection techniques from the filter and wrapper approaches and then used the best one to propose our implicit authentication method. The random forest classifier is used to evaluate these techniques. It is also used to achieve the classification task in our authentication method. Using a public dataset, the experimental results showed that the filter-based technique (i.e., rank aggregation) is the best feature selection to build an implicit authentication method for the smartphone environment. It showed accuracy results around 97.80% using only 25 features out of 53 features (i.e., require less mobile resources (memory and processing power) to authenticate users. At the same time, the results showed that our method has less error rate: 2.03 FAR, 0.04 FRR, and 1.04 ERR, comparing to the related work. These promising results would be used to develop a mobile application that allows implicit authentication of legitimate owners while avoiding the traditional authentication problems and using fewer smartphone resources. [ABSTRACT FROM AUTHOR]

Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia which is often challenging to diagnose because patients may be asymptomatic, and episodes are usually intermittent. In this paper we describe a method for identifying patients with a history of PAF using electrocardiogram (ECG) recordings during sinus rhythm. We analyzed, on a beat-to-beat basis, the P-waves in the sinus rhythm ECGs of 69 patients with a history of PAF and 59 healthy individuals. From each subject's P-waves, we calculated key electrocardiographic metrics, including some which are proposed here for the first time. Using means testing and feature selection methods, we discerned the features which were most useful for classification, and trained a Random Forest which identified PAF patients. Our approach achieved a 93.45% accuracy, sensitivity of 95.21%, and specificity of 91.40% using P-wave integral and novel amplitude and slope-based features which ranked highest in importance compared to other metrics from the literature. In particular, descriptive statistics of P-wave amplitudes, slopes, and integrals, were effective for identifying subjects with PAF history vs. healthy individuals. Our method has a high sensitivity and discrimination ability, with an AUC=0.9669 which is superior to others', and can thus be potentially valuable for the early identification of patients who are prone to episodes of PAF, even as part of the standard cardiological checkup that most adults undergo periodically. • Early identification of PAF during sinus rhythm with high accuracy. • P-wave signal metrics on a beat-to-beat basis are proposed. • P-wave amplitude, integral and slope features were ranked as the most important. • A RF classifier distinguishes PAF from healthy subjects with a state-of-the-art AUC. [ABSTRACT FROM AUTHOR]

Today, a significant number of biomedical data is generated continuously from various biomedical equipment and experiments due to rapid technological improvements in medical sciences. Effective biomedical data analysis, such as extracting biological and diagnostically significant features, is a very challenging task. This paper proposes hybrid Machine Learning Classification Techniques based on ensemble technique with Enhanced-Grey Wolf Optimization (E-GWO) feature selection algorithm to analyze these complex biomedical data. We combined five biomedical heart disease data sets for the experimental work: Cleveland, Long-Beach-VA, Switzerland, Hungarians, and Statlog. New hybrid Machine Learning Classification Techniques classifiers like Naive Bayes Bagging Technique (NBBT), Random Forest Bagging Technique (RFBT), Decision Tree Bagging Technique (DTBT), K-Nearest Neighbors Bagging Technique (KNNBT), Neural Network Bagging Technique (NNBT), Gradient Boosting Boosting Technique (GBBT), and Adaptive Boosting Boosting Technique (ABBT) are developed with bagging and boosting methods. Accuracy, Recall, Precision, F1-score, Specificity, Error Rate, G-mean, False Negative Rate (FNR), False Positive Rate (FPR), and Negative Predictive Value (NPV) are used to evaluate hybrid techniques. The experimental result shows that the developed hybrid classifier RFBT achieves the highest accuracy of 99.26% with E-GWO feature selection. The proposed method has 11.90% improved the accuracy to the conventional model. [ABSTRACT FROM AUTHOR]