  Mobile phones and other electronic gadgets or devices have aided in
collecting data without the need for data entry. This paper will specifically
focus on Mobile health data. Mobile health data use mobile devices to gather
clinical health data and track patient vitals in real-time. Our study is aimed
to give decisions for small or big sports teams on whether one athlete good fit
or not for a particular game with the compare several machine learning
algorithms to predict human behavior and health using the data collected from
mobile devices and sensors placed on patients. In this study, we have obtained
the dataset from a similar study done on mhealth. The dataset contains vital
signs recordings of ten volunteers from different backgrounds. They had to
perform several physical activities with a sensor placed on their bodies. Our
study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree,
Random Forest, and Logistic Regression) to analyze and predict human health
behavior. XGBoost performed better compared to the other machine learning
algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in
specificity, and 99.66% in F1 score. Our research indicated a promising future
in mhealth being used to predict human behavior and further research and
exploration need to be done for it to be available for commercial use
specifically in the sports industry.
