Machine Learning and Bioinformatics for Diagnosis Analysis of Obesity Spectrum Disorders

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.

Physiological Signal Processing in Heart Rate Variability Measurement: A Focus on Spectral Analysis

Fast Fourier Transform (FFT) relies on the HRV frequency-domain analysis techniques. It requires re-sampling of the inherently unevenly sampled heartbeat time-series (RR tachogram) to produce an evenly sampled time series of the heartbeat. However, re-sampling of the heartbeat time -- series is found to produce a substantial error when estimating an artificial RR tachogram.