KFPredict: An ensemble learning prediction framework for diabetes based on fusion of key features.

BACKGROUND AND OBJECTIVE: Diabetes is a disease that requires early detection and early treatment, and complications are likely to occur in late stages of the disease, threatening the life of patients. Therefore, in order to diagnose diabetic patients as early as possible, it is necessary to establish a model that can accurately predict diabetes. METHODOLOGY: This paper proposes an ensemble learning framework: KFPredict, which combines multi-input models with key features and machine learning algorithms. We first propose a multi-input neural network model (KF_NN) that fuses key features and uses a decision tree-based selection recursive feature elimination algorithm and correlation coefficient method to screen out the key feature inputs and secondary feature inputs in the model. We then ensemble KF_NN with three machine learning algorithms (i.e., Support Vector Machine, Random Forest and K-Nearest Neighbors) for soft voting to form our predictive classifier for diabetes prediction. RESULTS: Our framework demonstrates good prediction results on the test set with a sensitivity of 0.85, a specificity of 0.98, and an accuracy of 93.5%. Compared with the single prediction method KFPredict, the accuracy is up to 18.18% higher. Concurrently, we also compared KFPredict with the existing prediction methods. It still has good prediction performance, and the accuracy rate is improved by up to 14.93%. CONCLUSION: This paper constructs a diabetes prediction framework that combines multi-input models with key features and machine learning algorithms. Taking tthe PIMA diabetes dataset as the test data, the experiment shows that the framework presents good prediction results.

A Novel Method to Rank Influential Nodes in Complex Networks Based on Tsallis Entropy.

With the rapid development of social networks, it has become extremely important to evaluate the propagation capabilities of the nodes in a network. Related research has wide applications, such as in network monitoring and rumor control. However, the current research on the propagation ability of network nodes is mostly based on the analysis of the degree of nodes. The method is simple, but the effectiveness needs to be improved. Based on this problem, this paper proposes a method that is based on Tsallis entropy to detect the propagation ability of network nodes. This method comprehensively considers the relationship between a node's Tsallis entropy and its neighbors, employs the Tsallis entropy method to construct the TsallisRank algorithm, and uses the SIR (Susceptible, Infectious, Recovered) model for verifying the correctness of the algorithm. The experimental results show that, in a real network, this method can effectively and accurately evaluate the propagation ability of network nodes.

TW-SIR: time-window based SIR for COVID-19 forecasts.

Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries---China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.

Ultralow lattice thermal conductivity and high thermoelectric performance of monolayer KCuTe: a first principles study.

Monolayer KCuTe is a new-type of two-dimensional (2D) semiconductor material with high carrier mobility and large power energy conversion efficiencies, suggesting its potential application in thermoelectric (TE) and photoelectric fields. Based on the density functional theory (DFT) and semiclassical Boltzmann transport equation, the electronic and phonon transport properties of monolayer KCuTe are systematically studied. Our results show that it possesses an ultralow lattice thermal conductivity value of nearly ∼0.13 W m-1 K-1 at 300 K, mainly attributed to its small phonon group velocity, large Grüneisen parameters, and strong phonon-phonon scattering. Furthermore, the intralayer opposite phonon vibrations greatly restrict the heat transport. Monolayer KCuTe shows an ideal direct band gap of ∼1.21 eV, and a high twofold degeneracy appearing at the Γ point gives a high Seebeck coefficient of ∼2070 µV K-1, leading to high TE performance. Using the transport coefficients together with constant electron relaxation time, the figure of merit (ZT) can reach 2.71 at 700 K for the p-type doping, which is comparable to the well-known TE material SnSe (2.6 ± 0.3 at 935 K). Our theoretical studies may provide perspectives to TE applications of monolayer KCuTe and stimulate further experimental synthesis.