Medical Gas Pressure Level Monitoring System using Machine Learning Algorithms

• Published in: 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) pp. 207 - 214
• Main Authors: Yugapriya, M, Judeson Antony Kovilpillai, J, Jayanthy, S
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14-06-2023
• Subjects: Machine learning; Medical gas concentrator; Peripheral Interface Controller
• DOI: 10.1109/ICSCSS57650.2023.10169503

This research study proposes a medical gas pressure monitoring system that is developed to monitor the gas pressure level of each gas channel by measuring their pressure using an ABP series mount Pressure sensor. One of the greatest provocations happening in the health sector is the absence of a proper monitoring system of gas supply for patients. The proposed system uses a PIC16F877A microcontroller as the controller board. Totally three gas channels are taken into consideration. The pressure value of each gas channel will be displayed based on the unit which will be set by the user. The units are PSI, Kg, and bar by default it will display in PSI unit and threshold pressure levels of below 50 as a low level and above 75 as a high level, for each pressure level of low and high respected led will blink as an indication. Whenever the gas pressure level is reduced below 50 the led and buzzer will be on, the proposed system has a mute button and mode button, and the user can mute it down using the mute button, and goes like this for pressure levels above 75. If the value stays at the pressure level of below 50 or above 75 for more than 15 minutes again buzzer will be on. And by using the mode button the user can vary the threshold of all three gas pressure levels using the mode buttons provided in the controller unit. This pressure level of a single channel at the particular voltage and current level is acquired as a raw real-time dataset without any feature extraction. This raw data which contains real-time parameters such as pressure, voltage, and current is acquired at the microcontroller and as well at the gas outlet pipe from a single channel (oxygen gas cylinder). This study uses ML algorithms such as KNN, logistic regression, random forest, naïve Bayes, and decision trees that get trained by the raw data and will give the parameter matrices such as precision, accuracy, F1 score, and recall to evaluate the algorithm. Hence, the decision tree algorithm achieved the highest performance rate of 81%. So, the decision tree algorithm is found to be the best Machine learning model which got well trained by the raw train dataset.