Statistical Techniques of Exhaled Breath Analysis for Disease Diagnosis and Human Health Monitoring

Exhaled breath analysis finds patterns of volatile organic compounds in the human body. This paper describes the main types of sensors used for disease diagnosis applications of exhaled breath, data preprocessing, and data analysis techniques of breath data and includes the results of some existing research. First, enlist the sensor mainly used for identifying breath biomarkers. Next, data preprocessing removes the noise from the collected breath data. Then, machine learning algorithms are used to identify discriminators of volatile organic compounds after preprocessing breath data for data analysis. This work comprises data preprocessing and analysis using different breath analysis techniques and machine learning algorithms for real-life situations. The paper includes a brief description of each machine learning algorithm, process, algorithms' condition, and its relevant formula. This work systematically reviews breath data preprocessing and analysis techniques with advantages, disadvantages, applications and some disease results of existing research.