A Machine Learning Approach for Human Breath Diagnosis with Soft Sensors. (May 2022)

• Record Type: Journal Article
• Authors: Suresh, K.C.; Prabha, R.; Hemavathy, N.; Sivarajeswari, S; Gokulakrishnan, D; Jagadeesh kumar, M.
• Is Part Of: Computers & electrical engineering. Volume 100(2022)
• Publication Date: 2022-05
• Subjects: Bio Sensors -- Machine Learning -- Sensor Networks -- Electronics -- Communication -- Networks -- Regression -- SVM; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107945
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: This work explains the detection of diseases in the body of a human by a breath analysis process using linear regression techniques and a support vector machine. Currently, medical diagnosis is developed using various computing technologies, and proficient structure based on clinical symptoms is used to decide what type of disease is likely to come into view for a patient. The support vector machine with the linear regression model is applied to the patient's data, which we obtain as input from the patient's body through the biosensor and functions to the same data for predictions. The results obtained by the proposed diagnostic system clearly show minimized prediction errors compared to the traditional approach.