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click hereThesis Title: Determinants of Double and Triple Burden of Non-communicable Diseases and Its Prediction Using Machine Learning Techniques: Evidence from Bangladesh Demographic and Health Survey
Background
and objectives: Globally, non-communicable diseases
(NCDs) are a primary public health issue and have taken the top position in the
cause of death in Bangladesh. This study aimed to estimate the prevalence of
double burden of NCDs (DBNCDs) and triple burden of NCDs (TBNCDs) considering
hypertension, diabetics and overweight & obesity, and explore the risk
factors of DBNCDs and TBNCDs, as well as predict its performance using machine
learning techniques.
Materials
and methods: A total sample of 12,151 participants’
(5238 males and 6913 females) from 2017-18 Bangladesh Demographic and Health
Survey (BDHS) were included for the purpose of analysis. Descriptive statistics
were performed to calculate the distribution and prevalence of DBNCDs and
TBNCDs. Bivariate and multilevel logistic regression analysis were used to
assess the individual-and community-level determinants of DBNCDs and TBNCDs.
Furthermore, the study had adopted six classifiers like k-nearest neighbor
(KNN), decision tree (DT), logistic regression (LR), naive bayes (NB), random
forest (RF), extreme gradient boosting (XGBoost) to predict the DBNCDs and
TBNCDs. Three types of partition protocols (K2, K5 & K10) had also adopted
to measure the performance of six classifiers and repeated these protocols into
10 trials. The accuracy (ACC) and area under the curve (AUC) were used to assess
the performance of the classifiers.
Results:
The prevalence of DBNCDs and TBNCDs were 14.3% and 2.3%, respectively. At
individual-level, higher age, female gender, currently and formerly/ever
married, richest, higher educated were more likely to suffer from the DBNCDs
and TBNCDs. Furthermore, at community-level, division had significant
association with DBNCDs and TBNCDs. In addition, family size had significant
effect on DBNCDs and caffeinate drink and community poverty had significant
effect on TBNCDs. RF-based classifier gave highest
ACC and AUC for all the three partition protocols in case of both DBNCDs (for
K2, ACC = 77.88% & AUC = 0.91; for K5, ACC = 80.19% & AUC = 0.91; for
K10, ACC = 81.06% & AUC = 0.93) as well as TBNCDs ((for K2, ACC = 87.39%
& AUC = 0.96; for K5, ACC = 88.54% & AUC = 0.96; for K10, ACC = 88.61%
& AUC = 0.97).
Conclusion: This
study identifies several individual-and community level factors i.e. age,
gender, marital status, wealth index, education level and division which are
significantly associated with both DNCDs and TNCDs. Moreover, RF-based
classifier provided the best performance. Government and nongovernment health
organization should pay proper attention to handle the burden of NCDs in
Bangladesh.
Details | |||
Role | Supervisor | ||
---|---|---|---|
Class / Degree | Masters | ||
Students | Md. Akib Al-Zubayer (Student ID: MS 202013) | ||
Start Date | 2019 | ||
End Date | 2022 |