Mediation Analysis of Diabetes and Heart Diseases Influenced by Obesity Using Machine Learning Classifiers - Ajay Verma; Manisha Jain (Austrian Journal of Statistics)
Purpose: This study examines the impact of body mass index on diabetes and heart disease among Indians. Multi-morbidity ailments are associated with diabetes. To understand the relationship between diabetes, body mass index, and heart disease, a study is undertaken. Methods: The present study established a relationship between diabetes, heart disease and body mass index using mediation analysis and machine learning classifiers. R software with Hayes Macro Process and Python was used as a statistical tool to conclude the study. Results: As a result of the present findings, the body mass index mediates the relationship between diabetes and heart disease and cannot be countered. This study's indirect impact is 4.6231, statistically significant at a 95% confidence interval (3.1333, 9.6556). The significance of the indirect effect of diabetes on heart disease is evident as (BootLLCI), and (BootULCI) are both positive and do not contain zero. This indicates that there is a substantial mediation effect present. In classification, the TensorFlow classifier shows 99% accuracy and 97% precession, while the Linear S.V.C., NuSVC and Logistic Regression have an accuracy of 98%, 96% and 97%, which shows that the machine learning classifiers are more significant for the study. Conclusion: Our study examines how Body Mass Index (B.M.I.) mediates diabetes and heart disease, which are statistically significant. Despite the close relationship between heart disease and diabetes, little is known about the pathways involved. Machine Learning Classifiers show that the risk of diabetes, heart disease and other diseases increases due to deterioration of body mass index.