Machine Learning Based Prediction of Non-communicable Diseases to Improving Intervention Program in Bangladesh

Hu, Min; Nohara, Yasunobu; Wakata, Yoshifumi; Ahmed, Ashir; Nakashima, Naoki; Nakamura, Masafumi

doi:10.24105/ejbi.2018.14.4.5

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…[23]. In this study, health status is assessed depending on the results of health checkup items provided by the PHC service [28]. We used the "Bangladesh-logic (B-logic)" risk stratification (which was created based on the international diagnostic standards) technique to rank the risk levels of each health checkup item result into four categories: healthy, caution, affected, and emergent [2,22,29].…”

Section: Health Statusmentioning

confidence: 99%

Impacts of Anthropometric, Biochemical, Socio-Demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country

2020

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This study focused on urban corporate people and applied multinomial logistic regression (MLR) to identify the impact of anthropometric, biochemical, socio-demographic and dietary habit factors on health status. Health status is categorized into four levels: healthy, caution, affected, and emergent. A cross-sectional study, based on convenience sampling method, was conducted to select 271 employees from 18 institutions under the Grameen Bank Complex, Dhaka, Bangladesh. Biochemical measurements such as blood uric acid are highly significant variables in the MLR model. When holding other factors as constants, with a one-unit increase in blood uric acid, a person is 11.02 times more likely to be “emergent” compared to “caution”. The odds are also higher, at 1.82, for the blood uric acid to be “affected” compared “caution”. The results of this study can help to prevent a large proportion of non-communicable diseases (NCDs) by reducing the most significant risk factor: blood uric acid. This study can contribute to the establishment of combined actions to improve disease management.

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Section: Health Statusmentioning

confidence: 99%

Impacts of Anthropometric, Biochemical, Socio-Demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country

2020

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“…In many clinical studies, the gradient boosting machine-learning algorithm has been successfully used to predict cardiovascular diseases [13]. The gradient boosting decision tree method introduced by Friedman [68] predicted BMI with an accuracy of 0.91 [37]. In the current study, the boosted decision tree regression was found to be the best predictive model for uric acid, followed by decision forest regression.…”

Section: Principal Findingsmentioning

confidence: 62%

Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison (Preprint)

Sampa¹,

Hossain²,

Hoque³

et al. 2020

Preprint

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BACKGROUND Uric acid is associated with Non-communicable diseases (NCDs), such as cardiovascular diseases, chronic kidney disease, coronary artery disease, stroke, diabetes, metabolic syndrome, vascular dementia, and hypertension. Therefore, uric acid is considered to be a risk factor for the development of NCDs. Most studies on uric acid have been performed in developed countries. To our knowledge, the application of machine learning approaches in uric acid prediction in developing countries is rare. Different ML algorithms will work differently on different types of data in various diseases such as cancers, diabetes, therefore, a different investigation is needed for different types of data in order to identify the most accurate algorithms. Specifically, yet, no study focused on the urban corporate people in Bangladesh, though they are more likely to develop NCDs. OBJECTIVE The aim of this study is to use machine learning approaches to predict blood uric acid based on basic health checkup test results, dietary information, and socio-demographic characteristics. The prediction of health checkup test measurements is very helpful to reduce health management costs. METHODS This study used machine learning approaches because clinical input data are not completely independent and complex interactions exist between them. Conventional statistical models have limitations to consider these complex interactions but ML can consider all possible interactions between input data. This study used several machine learning approaches such as Boosted Decision Tree Regression, Decision Forest Regression, Bayesian Linear Regression, and Linear Regression to predict personalized blood uric acid based on basic health check-up test results, dietary information, and socio-demographic characteristics. We evaluated the performance of these five widely used machine learning models. Data have been collected from 271 employees, who work in the Grameen Bank complex, Dhaka, Bangladesh. RESULTS The mean of uric acid measurement was 6.62 mg/dL. That means the uric acid of most of the people is on the borderline (6.62 whereas the normal range <7.0 mg/dL). Therefore, they need to check uric acid regularly. The Boosted Decision Tree Regression model showed the best performance among other models based on the Root Mean Squared Error (RMSE) 0.03, this RMSE is better than any reported in the literature. CONCLUSIONS This study developed a uric acid prediction model based on personal characteristics, dietary information and some basic health checkup measurements. Such a uric acid prediction model is useful for improving awareness among high-risk subjects. By predicting uric acid, this study can help to save medical costs. A future study could include additional features (e.g. work stress, everyday physical activity, alcohol intake, eating red meat). CLINICALTRIAL The authors obtained ethical approval from the National Research Ethics Committee (NREC) of the Bangladesh Medical Research Council with approval no. 18325022019.

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“…In many clinical studies, Gradient boosting machine learning algorithm has been successfully used to predict cardiovascular diseases [11]. The gradient boosting decision tree (GBDT) method by Friedman [33] predicted BMI with accuracy 0.91 [34]. In the current study boosted decision tree regression is found as the best predictive model followed by decision forest regression.…”

Section: Discussionmentioning

confidence: 77%

A Machine Learning Model Based Web App to Predict Diabetic Blood Glucose

Sampa

Biswas

Hoque

et al. 2023

Preprint

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Aim of this study is to use machine learning approaches for predicting blood glucose based on basic non-invasive health checkup test results, dietary information, and socio-demographic characteristics and to develop a web application to predict blood glucose easily. We evaluated the performance of five widely used machine learning models. Data have been collected from 271 employees of Grameen Bank complex, in Dhaka, Bangladesh. This study used continuous blood glucose data to train the model and predicted new blood glucose values using the trained data. Finally, we developed a blood glucose prediction web application. The Boosted Decision Tree Regression model showed the best performance among other models based on the Root Mean Squared Error (RMSE) 2.30, this RMSE is better than any reported in the literature. This study developed a blood glucose prediction model and web application which is easier, more convenient, and more efficient for people. People can also easily check their blood glucose values using our app, especially in remote areas of developing countries that lack adequate skilled doctors and nurses. By predicting blood glucose, this study can help to save medical costs and time and to reduce health management costs. Our system can be helpful in achieving SDGs, Universal Health Coverage and thus reducing overall morbidity and mortality.

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Machine Learning Based Prediction of Non-communicable Diseases to Improving Intervention Program in Bangladesh

Cited by 10 publications

References 13 publications

Impacts of Anthropometric, Biochemical, Socio-Demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country

Impacts of Anthropometric, Biochemical, Socio-Demographic, and Dietary Habits Factors on the Health Status of Urban Corporate People in a Developing Country

Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison (Preprint)

A Machine Learning Model Based Web App to Predict Diabetic Blood Glucose

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