The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients’ outcome prediction into two steps. The first step is to extract the key features from the patients’ many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients’ outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article’s prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to traditional methods for clinical predictions because it is capable of processing large amounts of complex data. We, therefore, explored the feasibility of an ML approach for predicting outcomes in young patients with hypertension and compared its performance with that of approaches now commonly used in clinical practice. Baseline clinical data and a composite end point—comprising all-cause death, acute myocardial infarction, coronary artery revascularization, new-onset heart failure, new-onset atrial fibrillation/atrial flutter, sustained ventricular tachycardia/ventricular fibrillation, peripheral artery revascularization, new-onset stroke, end-stage renal disease—were evaluated in 508 young patients with hypertension (30.83±6.17 years) who had been treated at a tertiary hospital. Construction of the ML model, which consisted of recursive feature elimination, extreme gradient boosting, and 10-fold cross-validation, was performed at the 33-month follow-up evaluation, and the model’s performance was compared with that of the Cox regression and recalibrated Framingham Risk Score models. An 11-variable combination was considered most valuable for predicting outcomes using the ML approach. The C statistic for identifying patients with composite end points was 0.757 (95% CI, 0.660–0.854) for the ML model, whereas for Cox regression model and the recalibrated Framingham Risk Score model it was 0.723 (95% CI, 0.636–0.810) and 0.529 (95% CI, 0.403–0.655). The ML approach was comparable with Cox regression for determining the clinical prognosis of young patients with hypertension and was better than that of the recalibrated Framingham Risk Score model.

ObjectiveTo determine the incidence of amyotrophic lateral sclerosis (ALS) in Beijing from 2010 to 2015 and to address the issue of prognosis.MethodsThe number of patients diagnosed with ALS was generated from two aspects, namely, diagnostic hospitals and assisted care institutions. By examining the consistency of the overlapping data in terms of age and gender distributions, the number of ALS patients in Beijing was estimated to analyze the incidence. Finally, a prognosis study was carried out by sorting the clinical data of deceased patients to associate time to death with the demographic characteristics, including gender, age at diagnosis, site of onset, body mass index, and lag from onset to diagnosis.ResultsThe average yearly incidence was 0.8/100,000 persons, the male–female ratio was 1.63:1, and the mean age at diagnosis was 54.11 years. The mean time from symptom onset to diagnosis was 14.8 months, and the median survival time from diagnosis was 49.4 months. In addition, each of the identified clinical features was related to the survival of the patients with ALS.ConclusionsThe incidence of ALS in Beijing is similar to the rates in Hong Kong and Taiwan but is lower than the rates in Europe and America. In addition, the mean age at onset of the patients in Beijing was early, and overall ALS prognosis appears to be comparable to those reported in recent publications.

The changes in people's life rhythm and improvement in material levels that happened in recent years increased the number of people suffering from high blood pressure in the world. Therefore, as a cardiac complication of hypertension, the prevalence of hypertensive heart disease has increased annually, it has seriously endangered the safety of human life, and the effective prediction of hypertensive heart disease has become a worldwide problem. This paper uses the newly proposed XGBSVM hybrid model to predict whether hypertensive patients will develop hypertensive heart disease within three years. The final experiment proves that through this model, hypertensive patients can learn their risk of hypertensive heart disease within 3 years and then undergo targeted preventive treatment, thereby reducing the psychological, physiological and economic burden. This paper confirms that the machine learning can be successfully applied in the biomedical field, with strong real-world significance and research value.