Predicting the risk of diabetic retinopathy using explainable machine learning algorithms.

BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR) is a global health concern among diabetic patients. The objective of this study was to propose an explainable machine learning (ML)-based system for predicting the risk of DR. MATERIALS AND METHODS: This study utilized publicly available cross-sectional data in a Chinese cohort of 6374 respondents. We employed boruta and least absolute shrinkage and selection operator (LASSO) based feature selection methods to identify the common predictors of DR. Using the identified predictors, we trained and optimized four widly applicable models (artificial neural network, support vector machine, random forest, and extreme gradient boosting (XGBoost) to predict patients with DR. Moreover, shapely additive explanation (SHAP) was adopted to show the contribution of each predictor of DR in the prediction. RESULTS: Combining Boruta and LASSO method revealed that community, TCTG, HDLC, BUN, FPG, HbAlc, weight, and duration were the most important predictors of DR. The XGBoost-based model outperformed the other models, with an accuracy of 90.01%, precision of 91.80%, recall of 97.91%, F1 score of 94.86%, and AUC of 0.850. Moreover, SHAP method showed that HbA1c, community, FPG, TCTG, duration, and UA1b were the influencing predictors of DR. CONCLUSION: The proposed integrating system will be helpful as a tool for selecting significant predictors, which can predict patients who are at high risk of DR at an early stage in China.

Socioeconomic inequality in the prevalence of low birth weight and its associated determinants in Bangladesh.

BACKGROUND AND OBJECTIVE: Low birth weight (LBW) is a major risk factor of child mortality and morbidity during infancy (0-3 years) and early childhood (3-8 years) in low and lower-middle-income countries, including Bangladesh. LBW is a vital public health concern in Bangladesh. The objective of the research was to investigate the socioeconomic inequality in the prevalence of LBW among singleton births and identify the significantly associated determinants of singleton LBW in Bangladesh. MATERIALS AND METHODS: The data utilized in this research was derived from the latest nationally representative Bangladesh Demographic and Health Survey, 2017-18, and included a total of 2327 respondents. The concentration index (C-index) and concentration curve were used to investigate the socioeconomic inequality in LBW among the singleton newborn babies. Additionally, an adjusted binary logistic regression model was utilized for calculating adjusted odds ratio and p-value (<0.05) to identify the significant determinants of LBW. RESULTS: The overall prevalence of LBW among singleton births in Bangladesh was 14.27%. We observed that LBW rates were inequitably distributed across the socioeconomic groups (C-index: -0.096, 95% confidence interval: [-0.175, -0.016], P = 0.029), with a higher concentration of LBW infants among mothers living in the lowest wealth quintile (poorest). Regression analysis revealed that maternal age, region, maternal education level, wealth index, height, age at 1st birth, and the child's aliveness (alive or died) at the time of the survey were significantly associated determinants of LBW in Bangladesh. CONCLUSION: In this study, socioeconomic disparity in the prevalence of singleton LBW was evident in Bangladesh. Incidence of LBW might be reduced by improving the socioeconomic status of poor families, paying special attention to mothers who have no education and live in low-income households in the eastern divisions (e.g., Sylhet, Chittagong). Governments, agencies, and non-governmental organizations should address the multifaceted issues and implement preventive programs and policies in Bangladesh to reduce LBW.

Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh.

BACKGROUND AND AIMS: Hypertension has become a major public health issue as the prevalence and risk of premature death and disability among adults due to hypertension has increased globally. The main objective is to characterize the risk factors of hypertension among adults in Bangladesh using machine learning (ML) algorithms. MATERIALS AND METHODS: The hypertension data was derived from Bangladesh demographic and health survey, 2017-18, which included 6965 people aged 35 and above. Two most promising risk factor identification methods, namely least absolute shrinkage operator (LASSO) and support vector machine recursive feature elimination (SVMRFE) are implemented to detect the critical risk factors of hypertension. Additionally, four well-known ML algorithms as artificial neural network, decision tree, random forest, and gradient boosting (GB) have been used to predict hypertension. Performance scores of these algorithms were evaluated by accuracy, precision, recall, F-measure, and area under the curve (AUC). RESULTS: The results clarify that age, BMI, wealth index, working status, and marital status for LASSO and age, BMI, marital status, diabetes and region for SVMRFE appear to be the top-most five significant risk factors for hypertension. Our findings reveal that the combination of SVMRFE-GB gives the maximum accuracy (66.98%), recall (97.92%), F-measure (78.99%), and AUC (0.669) compared to others. CONCLUSION: GB-based algorithm confirms the best performer for prediction of hypertension, at an early stage in Bangladesh. Therefore, this study highly suggests that the policymakers make proper judgments for controlling hypertension using SVMRFE-GB-based combination to save time and reduce cost for Bangladeshi adults.

Prevalence and Severity of COVID-19 Disease in Bangladesh: A Trend Analysis / Prevalência e gravidade da COVID-19 em Bangladesh: Uma análise de tendências

Objective: To investigate the prevalence and changes of events of COVID-19 disease by trending in Bangladesh. Methods: In this study, the daily time series data for nine weeks was used. The daily cases, case fatality rate (CFR), recovery-death-ratio (RDR) and percent changes (PC) associated with COVID-19 disease were used for prevalence and trending. Result: It is found that 68% males and 32% females patients were infected, among them 21 to 30 (26%) was the most and below 10 (3%) was the least infected age group until May 09. The approximate number of days for the infection, recovery and deaths to be doubled are 10, 5 and 18 respectively in Bangladesh as of May 09, 2020. The CFR of Bangladesh is found 1.55% which is less than the CFRs of the world (6.89%), Europe (9.17%), America (5.61%), Africa (3.26%) and South-East Asia (3.52%) as of May 09, 2020. The daily RDR exhibited a downward trend from April 04, 2020 to April 25, 2020 then showed an upward trend until May 09, 2020. Conclusion: The downward trending of the CFR indicates the death rate is low compared to diagnosis. The upward trend of the RDR indicates the recovery caused by COVID-19 is fast compared to deaths over time in Bangladesh. The downward trending of the PC indicates the cases percent of COVID-19 disease is reducing relative to three days prior cases.

Objetivo: investigar a prevalência e as alterações da COVID-19 em Bangladesh. Métodos: foram utilizados os dados diários das séries temporais por nove semanas. Os casos diários, taxa de fatalidade de casos (CFR), razão de recuperação-morte (RDR) e alterações percentuais (CP) associadas à COVID-19 foram utilizados para calcudo da prevalência e tendências da doença. Resultados: verificou-se que 68% dos pacientes do sexo masculino e 32% do sexo feminino estavam infectados, entre eles, 21 a 30 (26%) era a faixa etária mais abaixo e 10 (3%) era a menos infectada até nove de maio. O número aproximado de dias para duplicação da infecção, recuperação e mortes foi de 10, 5 e 18, respectivamente, em Bangladesh, a partir de nove de maio de 2020. O CFR de Bangladesh, até nove de maio, foi de 1,55%, inferior aos CFRs do mundo (6,89%), Europa (9,17%), América (5,61%), África (3,26%) e Sudeste da Ásia (3,52%). O RDR diário exibiu uma tendência de queda de quatro de abril de 2020 a 25 de abril de 2020 e, em seguida, mostrou uma tendência de alta até nove de maio de 2020. Conclusão: a tendência descendente do CFR indica que a taxa de mortalidade é baixa em comparação com o diagnóstico. A tendência ascendente do RDR indica que a recuperação causada pelo COVID-19 é rápida, em comparação com as mortes, ao longo do tempo, em Bangladesh. A tendência de queda do PC indica que a porcentagem de casos de COVID-19 está diminuindo em relação aos três dias anteriores.