Symptoms Analysis Based Chronic Obstructive Pulmonary Disease Prediction In Bangladesh Using Machine Learning Approach

2021 ◽  
Author(s):  
Mridul Das Joshe ◽  
Nazmul Hassan Emon ◽  
Mirajul Islam ◽  
Nushrat Jahan Ria ◽  
Abu Kaisar Mohammad Masum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Learning Approach ◽  
Disease Prediction ◽  
Obstructive Pulmonary Disease ◽  
Machine Learning Approach

scholarly journals A machine learning approach to triaging patients with chronic obstructive pulmonary disease

PLoS ONE ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. e0188532 ◽  
Author(s):  
Sumanth Swaminathan ◽  
Klajdi Qirko ◽  
Ted Smith ◽  
Ethan Corcoran ◽  
Nicholas G. Wysham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Learning Approach ◽  
Obstructive Pulmonary Disease ◽  
Machine Learning Approach

scholarly journals Author Correction: A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Junfeng Peng ◽  
Chuan Chen ◽  
Mi Zhou ◽  
Xiaohua Xie ◽  
Yuqi Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Acute Exacerbation ◽  
Chronic Obstructive ◽  
Learning Approach ◽  
Clinical Indicators ◽  
Obstructive Pulmonary Disease ◽  
Machine Learning Approach

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Prediction of chronic obstructive pulmonary disease stages using machine learning algorithms

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Predictive Value ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Chronic Obstructive ◽  
Optimal Prediction ◽  
Obstructive Pulmonary Disease ◽  
Disease Stages

Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and, to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The F1 score, specificity, sensitivity, accuracy, positive predictive value and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients’ records. Our results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy and hence it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD.

Diagnosing asthma and chronic obstructive pulmonary disease with machine learning

2017 ◽  
Vol 25 (3) ◽  
pp. 811-827 ◽  
Author(s):  
Dimitris Spathis ◽  
Panayiotis Vlamos
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Random Forest ◽  
Pulmonary Disease ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Forced Expiratory Volume ◽  
Random Forest Classifier ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease

This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease’s case, Random Forest classifier outperforms other techniques with 97.7 per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. In asthma’s case, the best precision, 80.3 per cent, is achieved again with the Random Forest classifier, while the most prominent attribute is MEF2575.

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