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Breast cancer is a significant health problem, with about 2 million new cases annually diagnosed and 600,000 deaths. Early detection and accurate diagnosis are critical to patient prognosis. Machine learning (ML) models show promising results in accurate and efficient diagnosis. In the present work, the performance of different models of ML are studied in the publicly accessible online dataset "Wisconsin Breast Cancer Dataset". Those models are formed by logistic regressions, Random Forest, Naïve Bayes, and Support Vector Machine algorithms, being the last one the best performing. An ensemble model combining the best proposed models is then implemented. An SVM model with standardized dataset is used, a logistic regression model with standardized dataset and 10-component PCA analysis. A Random Forest model with standardized dataset and 60 estimators. All models use a test dataset formed by 30% of the original dataset. The models are combined using a majority weighted voting system. The SVM model has a weight of 0.5 while the regression and Random Forest models have weights of 0.25. The ensemble voting model manages to improve the results of the individual models with an accuracy of 98%, precision of 97%, recall of 99% and F1 score of 98%.

All universities and colleges worldwide had been closed due to the coronavirus outbreak. Due to the unexpected growth of online education, many teachers now conduct classes remotely using electronic devices like laptops and mobile phones to deliver instruction on digital platforms. Numerous studies have linked excessive or extended screen time to negative psychological and physiological outcomes. Stress, sadness, sleep difficulties, effects on eyesight, posture pain, hand limitations, and musculoskeletal issues are all mental and physical ailments. This research aims to examine how the COVID-19 epidemic has affected students' attendance and performance in online courses. The survey was administered online using Google forms. Undergraduates of both sexes between the ages of 18 and 25 who were enrolled in online courses for at least one month were considered for participation. In contrast, their physical health was evaluated using the Neck disability index and the Oswestry low back pain disability index. Statistical Methods Understood Karl Pearson's correlation coefficient was used to examine the relationship between the duration of an online course and feelings of anxiety, depression, and pain in the neck, shoulders, and back. Researchers showed that online learning harmed students' mental and physical health, with 57.3% of the student body reporting high levels of stress, 32.5% reporting mild depression, 48.5% reporting mild neck aches, and 41.3% reporting moderate back pain. Students' emotional and physical well-being has suffered due to the increased time they spend online for completing their work on computers and smartphones.

Producción Científica, Alzheimer’s disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability., Ministerio de Industria, Energía y Turismo (AAL-20125036)

New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors’ knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario., Highlights • An automated system to support the diagnosis of COVID-19 patients using EfficientNet. • The proposed method has been validated using 10-fold cross-validation. • An external dataset also has been used for validation. • An accuracy of 99.62% and 96.70% is reported for binary and multi-class classification.

The real commercialisminformation often demonstrates temporal feature and time varying behavior. Temporal affiliation rule has thus got an active area of explore. A calendar part such as months and days, clock parts such as hours and seconds and differentiated units such as business days and academic years, act a major role in a wide range of information system applications. The calendar-based form has already been proposed by explorers to restrict the time-based association ships. This paper advises a novel algorithmic program to determine association rule on time dependent data employingeffective T tree and P-tree data structures. The algorithm complicates the significant advantage in terms of time and memory while comprising time dimension. Our approach path of scanning based on time-intervals yields littlerinformation set for a given valid interval thus cutting down the processing time. This approach is enforced on a synthetic data-set and result shows that temporal TFP tree collapses better performance over a TFP tree access.