Classify the Outcome of Arterial Blood Gas Test to Detect the Respiratory Failure Using Machine Learning

Analysis of Arterial Blood Gas (ABG) is an important investigation to measure oxygenation and blood acid levels. It is crucial in measuring the clinical status and contributes to an efficient and effective healthcare plan. Generally, ABG is applied in the emergency care units (ECU) and intensive car...

Full description

Saved in:

Bibliographic Details
Published in:	2022 International Conference on Decision Aid Sciences and Applications (DASA) pp. 1139 - 1143
Main Authors:	Kajanan, S., Kumara, B. T. G. S, Banujan, Kuhaneswaran, Prasanth, Senthan, Manitheepan, K.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 23-03-2022
Subjects:	AdaBoost Arterial Blood Gas Boosting Deep learning Error analysis Machine Learning Measurement Predictive models Respiratory Failure Support vector machines Training XGBoost
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Analysis of Arterial Blood Gas (ABG) is an important investigation to measure oxygenation and blood acid levels. It is crucial in measuring the clinical status and contributes to an efficient and effective healthcare plan. Generally, ABG is applied in the emergency care units (ECU) and intensive care units (ICU). Most of the time, the doctors and nurses have difficulties identifying the type of respiratory failure with the help of ABG test results. So, during this research with the adaption of certain supervised machine learning approaches, namely Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Catboost, Random Forest, Naïve Bayes, Support Vector Machine (SVM), LightGBM, K-Nearest Neighbors (KNN), Neural Network (NN) and Decision Tree and have been incorporated with the intension of identifying the type of the respiratory failure with the highest accurate technique. To fulfil this purpose, 700 patient test results have been obtained from a public hospital in Sri Lanka. From the results discovered, XGBoost outperformed against all other techniques in identifying the type of respiratory failure with the highest accuracy of 98.65% and the lowest error rate of 1.35%. To ensure whether the XGBoost outperformed against the different percentages of training and testing data, K-fold cross-validation with five folds also has been performed with the dataset. The cross-validation produces results with an accuracy of 98.45% and the lowest error rate of 1.55%. In conclusion, XGBoost has been utilised in developing the prediction model. This would be a promising start for a future research scholar to adopt the hybrid techniques and the deep learning techniques to identify the causes of respiratory failure and the prediction of the type of respiratory failure.
DOI:	10.1109/DASA54658.2022.9765012