Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data

We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than 104. Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes

Electromagnetic noise in electric circuits: Ringing and resonance phenomena in the common mode

It is generally believed that electromagnetic noise originates from the coupling of electric signals in a circuit with electric signals in surrounding materials in the environment. However, the noise phenomenon had not been quantified until now. In order to study the phenomenon of noise, we considered a standard circuit (two transmission lines), to which an additional transmission line was introduced in order to explicitly take into account the effect of conductors in the environment. We performed calculations using a newly developed multiconductor transmission-line theory for the resulting three-line circuit in order to determine the magnitude of the coupling between the circuit and the conductors in the environment under various conditions. We observed ringing and resonance phenomena in the common mode, which influenced the performance of the normal mode as electromagnetic noise. Our findings were confirmed by recent experiments in which conductor lines were arranged in various ways using a printed circuit board (PCB). The ordinary usage of electricity in the standard electric circuit was found to be worst in exciting the common mode noise