  Globally, the number of obese patients has doubled due to sedentary
lifestyles and improper dieting. The tremendous increase altered human
genetics, and health. According to the world health organization, Life
expectancy dropped from 80 to 75 years, as obese people struggle with different
chronic diseases. This report will address the problems of obesity in children
and adults using ML datasets to feature, predict, and analyze the causes of
obesity. By engaging neural ML networks, we will explore neural control using
diffusion tensor imaging to consider body fats, BMI, waist \& hip ratio
circumference of obese patients. To predict the present and future causes of
obesity with ML, we will discuss ML techniques like decision trees, SVM, RF,
GBM, LASSO, BN, and ANN and use datasets implement the stated algorithms.
Different theoretical literature from experts ML \& Bioinformatics experiments
will be outlined in this report while making recommendations on how to advance
ML for predicting obesity and other chronic diseases.
