Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network.

In recent decades, heart disease threatens people's health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease.

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network.

The electrocardiogram (ECG) is one of the most powerful tools used in hospitals to analyze the cardiovascular status and check health, a standard for detecting and diagnosing abnormal heart rhythms. In recent years, cardiovascular health has attracted much attention. However, traditional doctors' consultations have disadvantages such as delayed diagnosis and high misdiagnosis rate, while cardiovascular diseases have the characteristics of early diagnosis, early treatment, and early recovery. Therefore, it is essential to reduce the misdiagnosis rate of heart disease. Our work is based on five different types of ECG arrhythmia classified according to the AAMI EC57 standard, namely, nonectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beat. This paper proposed a high-accuracy ECG arrhythmia classification method based on convolutional neural network (CNN), which could accurately classify ECG signals. We evaluated the classification effect of this classification method on the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) based on the MIT-BIH arrhythmia database. According to the results, the proposed method achieved 99.8% accuracy, 98.4% sensitivity, 99.9% specificity, and 98.5% positive prediction rate for detecting VEB. Detection of SVEB achieved 99.7% accuracy, 92.1% sensitivity, 99.9% specificity, and 96.8% positive prediction rate.

In situ collection of dust grains falling from Saturn's rings into its atmosphere.

Saturn's main rings are composed of >95% water ice, and the nature of the remaining few percent has remained unclear. The Cassini spacecraft's traversals between Saturn and its innermost D ring allowed its cosmic dust analyzer (CDA) to collect material released from the main rings and to characterize the ring material infall into Saturn. We report the direct in situ detection of material from Saturn's dense rings by the CDA impact mass spectrometer. Most detected grains are a few tens of nanometers in size and dynamically associated with the previously inferred "ring rain." Silicate and water-ice grains were identified, in proportions that vary with latitude. Silicate grains constitute up to 30% of infalling grains, a higher percentage than the bulk silicate content of the rings.

[The relationship between tumor angiogenesis and cellular proliferation activity in nasal inverted papilloma].

OBJECTIVE: To investigate the expression of CD34 and proliferating cell nuclear antigen (PCNA) and to study the relationship between tumor angiogenesis and cellular proliferation activity in nasal inverted papilloma (NIP). METHOD: The expression of CD34 protein and proliferating cell nuclear antigen were detected in 30 cases of NIP, 16 cases of nasal polyps (NP) and 11 cases of nasal squamous cell carcinoma (NSCC) with immunohistochemical S-P method and the tumor angiogenesis (as assessed by MVD) and cellular proliferation activity (as assessed by PCNA labeling index, PCNA-LI) was evaluated. RESULT: A significant difference in CD34 expression was found among the three diseases above mentioned. The MVD were 32.41 +/- 5.68; 53.22 +/- 9.31 and 80.37 +/- 10.21 in NP, NIP and NSCC. The expression rate of PCNA (PCNA-LI) was 28.67 +/- 9.29; 35.35 +/- 7.02 and 63.18 +/- 12.17 in NP, NIP and NSCC (P<0.01). There was a significant difference in PCNA expression among the three diseases (P<0.01). A significant positive correlation was found between MVD and PCNA-LI (r=0.56, P<0.01). Tumors with a low PCNA-LI was associated with low MVD and vice versa. CONCLUSION: With a significant relation between tumor angiogenesis, cellular proliferation activity and pathological types, MVD, PCNA-LI can be used as a strong independent indicator to judge tumor aggressive behavior, proliferative activity, development of local or regional metastases and prognosis in nasal inverted papilloma.