Developing a nomogram for predicting depression in diabetic patients after COVID-19 using machine learning

ObjectiveThis study identified major risk factors for depression in community diabetic patients using machine learning techniques and developed predictive models for predicting the high-risk group for depression in diabetic patients based on multiple risk factors.MethodsThis study analyzed 26,829 adults living in the community who were diagnosed with diabetes by a doctor. The prevalence of a depressive disorder was the dependent variable in this study. This study developed a model for predicting diabetic depression using multiple logistic regression, which corrected all confounding factors in order to identify the relationship (influence) of predictive factors for diabetic depression by entering the top nine variables with high importance, which were identified in CatBoost.ResultsThe prevalence of depression was 22.4% (n = 6,001). This study calculated the importance of factors related to depression in diabetic patients living in South Korean community using CatBoost to find that the top nine variables with high importance were gender, smoking status, changes in drinking before and after the COVID-19 pandemic, changes in smoking before and after the COVID-19 pandemic, subjective health, concern about economic loss due to the COVID-19 pandemic, changes in sleeping hours due to the COVID-19 pandemic, economic activity, and the number of people you can ask for help in a disaster situation such as COVID-19 infection.ConclusionIt is necessary to identify the high-risk group for diabetes and depression at an early stage, while considering multiple risk factors, and to seek a personalized psychological support system at the primary medical level, which can improve their mental health

Development of Keyword Trend Prediction Models for Obesity Before and After the COVID-19 Pandemic Using RNN and LSTM: Analyzing the News Big Data of South Korea

The Korea National Health and Nutrition Examination Survey (2020) reported that the prevalence of obesity (≥19 years old) was 31.4% in 2011, but it increased to 33.8% in 2019 and 38.3% in 2020, which confirmed that it increased rapidly after the outbreak of COVID-19. Obesity increases not only the risk of infection with COVID-19 but also severity and fatality rate after being infected with COVID-19 compared to people with normal weight or underweight. Therefore, identifying the difference in potential factors for obesity before and after the pandemic is an important issue in health science. This study identified the keywords and topics that were formed before and after the COVID-19 pandemic in the South Korean society and how they had been changing by conducting a web crawling of South Korea's news big data using “obesity” as a keyword

Optimized Reversible Logic Multiplexer Designs for Energy-Efficient Nanoscale Computing

Nano- and quantum-based low-power applications are where reversible logic really shines. By using digitally equivalent circuits with reversible logic gates, energy savings may be achieved. Reducing garbage output and ancilla inputs is a primary emphasis of this study, which aims to lower power consumption in reversible multiplexers. Multiplexers with switchable 2:1, 4:1, and 8:1 ratios may be built using the SJ gate and other simple reversible logic gates. The number of ancilla inputs has been cut in half from four to zero, and the amount of garbage output has been cut in half as well, from eight to three, making the 2:1 multiplexer an improvement over the prior design. New 4:1 multiplexer has 10' ancilla inputs, up from 2' in the previous designs. The proposed 4:1 multiplexer also cuts waste production in half from the current 5-to-6 bins per day. The 8:1 multiplexer has two ancilla inputs and nine trash outputs, while the current architecture only has one of each. The functionality of the VHDL and Xilinx 14.7-coded designs is validated by ISIM simulations

Empowering energy with a cutting-edge reversible logic framework for universal shift registers

In order to meet the increasing need for reduced power consumption, reduced circuit size, and increased operating speed, this research focuses on the efficient design of reversible shift registers. The shift register is an essential building block of both arithmetic logic units (ALUs) and reversible memory circuits. In this research, an improved layout is shown for a variety of shift register types, including those with serial input and parallel output (SIPO), serial input and serial output (SISO), parallel input and parallel output (PIPO), parallel input and serial output (PISO), and universal shift registers (USR). Our approaches lessen waste in compared to standard procedures by maximizing '0′ GO and reducing CI. The 4-bit SIPO register has a 70 % increase in CI performance over previous designs and also provides SISO capability with a 50 % CI and 100 % GO boost. Our proposed designs for the SIPO, SISO, PIPO, and PISO registers also result in a 70 % and 80 % improvement in CI and GO for the 4-bit PISO register. We enhance the 4-bit Universal Shift Register (USR) design by 62.5 % in CI, 50 % in CI, 12.5 % in GO, and 56.25 in GO over the previous design

Exploring the Predictors of Rapid Eye Movement Sleep Behavior Disorder for Parkinson’s Disease Patients Using Classifier Ensemble

The rapid eye movement sleep behavior disorder (RBD) of Parkinson’s disease (PD) patients can be improved with medications such as donepezil as long as it is diagnosed with a thorough medical examination, since identifying a high-risk group of RBD is a critical issue to treat PD. This study develops a model for predicting the high-risk groups of RBD using random forest (RF) and provides baseline information for selecting subjects for polysomnography. Subjects consisted of 350 PD patients (Parkinson’s disease with normal cognition (PD-NC) = 48; Parkinson’s disease with mild cognitive impairment (PD-MCI) = 199; Parkinson’s disease dementia (PDD) = 103) aged 60 years and older. This study compares the prediction performance of RF, discriminant analysis, classification and regression tree (CART), radial basis function (RBF) neural network, and logistic regression model to select a final model with the best model performance and presents the variable importance of the final model’s variable. As a result of analysis, the sensitivity of RF (79%) was superior to other models (discriminant analysis = 14%, CART = 32%, RBF neural network = 25%, and logistic regression = 51%). It was confirmed that age, the motor score of Untitled Parkinson’s Disease Rating (UPDRS), the total score of UPDRS, the age when a subject was diagnosed with PD first time, the Korean Mini Mental State Examination, and Korean Instrumental Activities of Daily Living, were major variables with high weight for predicting RBD. Among them, age was the most important factor. The model for predicting Parkinson’s disease RBD developed in this study will contribute to the screening of patients who should receive a video-polysomnography

Predicting the Swallow-Related Quality of Life of the Elderly Living in a Local Community Using Support Vector Machine

Background and Objectives: This study developed a support vector machine (SVM) algorithm-based prediction model with considering influence factors associated with the swallowing quality-of-life as the predictor variables and provided baseline information for enhancing the swallowing quality of elderly people’s lives in the future. Methods and Material: This study sampled 142 elderly people equal to or older than 65 years old who were using a senior welfare center. The swallowing problem associated quality of life was defined by the swallowing quality-of-life (SWAL-QOL). In order to verify the predictive power of the model, this study compared the predictive power of the Gaussian function with that of a linear algorithm, polynomial algorithm, and a sigmoid algorithm. Results: A total of 33.9% of the subjects decreased in swallowing quality-of-life. The swallowing quality-of-life prediction model for the elderly, based on the SVM, showed both preventive factors and risk factors. Risk factors were denture use, experience of using aspiration in the past one month, being economically inactive, having a mean monthly household income <2 million KRW, being an elementary school graduate or below, female, 75 years old or older, living alone, requiring time for finishing one meal on average ≤15 min or ≥40 min, having depression, stress, and cognitive impairment. Conclusions: It is necessary to monitor the high-risk group constantly in order to maintain the swallowing quality-of-life in the elderly based on the prevention and risk factors associated with the swallowing quality-of-life derived from this prediction model

Comparing Ensemble-Based Machine Learning Classifiers Developed for Distinguishing Hypokinetic Dysarthria from Presbyphonia

It is essential to understand the voice characteristics in the normal aging process to accurately distinguish presbyphonia from neurological voice disorders. This study developed the best ensemble-based machine learning classifier that could distinguish hypokinetic dysarthria from presbyphonia using classification and regression tree (CART), random forest, gradient boosting algorithm (GBM), and XGBoost and compared the prediction performance of models. The subjects of this study were 76 elderly patients diagnosed with hypokinetic dysarthria and 174 patients with presbyopia. This study developed prediction models for distinguishing hypokinetic dysarthria from presbyphonia by using CART, GBM, XGBoost, and random forest and compared the accuracy, sensitivity, and specificity of the development models to identify the prediction performance of them. The results of this study showed that random forest had the best prediction performance when it was tested with the test dataset (accuracy = 0.83, sensitivity = 0.90, and specificity = 0.80, and area under the curve (AUC) = 0.85). The main predictors for detecting hypokinetic dysarthria were Cepstral peak prominence (CPP), jitter, shimmer, L/H ratio, L/H ratio_SD, CPP max (dB), CPP min (dB), and CPPF0 in the order of magnitude. Among them, CPP was the most important predictor for identifying hypokinetic dysarthria

Association among smoking, depression, and anxiety: findings from a representative sample of Korean adolescents

This study investigated the relationship between smoking and depression and anxiety using data from a nationwide survey representing Korean adolescents. Subjects were 6,489 adolescents in middle and high school (age 13–18) who had participated in the 2011 Korean Study of Promotion Policies on Children and Adolescents—Mental Health (KSPCAM). Daily smoking number of times for current smokers was classified as 1–2 times, 2–4 times and over 5 times. The odds ratio for the statistical test was presented using hierarchical logistic regression. When adjusted for covariates (gender, age, household economy, type of residing city, type of school, school record, satisfaction with school life, subjective health status, satisfaction with relationship with parents, and drinking experience), smokers more significantly likely to have depression (OR = 1.27, 95% CI [1.02–1.57]), and anxiety (OR = 1.49, 95% CI [1.14–1.96]) than non-smokers (p < 0.05). In addition, adolescents who smoke more than 5 cigarettes a day were 1.5 times more likely to have depression (OR = 1.48, 95% CI [1.13–1.92]) and anxiety (OR = 1.49, 95% CI [1.07–2.08]) than those who do not smoke. Smoking in adolescence was found to be significantly related with depression and anxiety. To promote the mental health of adolescents, effective smoking cessation programs are required