An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning

Khasha, Roghaye; Sepehri, Mohammad Mehdi; Mahdaviani, Seyed Alireza

doi:10.1007/s10916-019-1259-8

Cited by 19 publications

(36 citation statements)

References 33 publications

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“…Therefore, physicians' expertise was combined with an ensemble ML algorithm to detect asthma control. The optimal accuracy of the model was 91.66% and, although the study included relatively few factors affecting asthma control, the model could help clinicians develop timely treatment plans 67 . Based on these findings, several common supervised ML algorithms were further used to analyze asthmatic monitoring data from 5,875 patients enrolled in the Asthma Mobile Health Study.…”

Section: Ai/ml and Asthmamentioning

confidence: 99%

“…Despite these current limitations, AI/ML techniques are needed in the medical field due to the special ability to efficiently analyze and integrate large and heterogeneous data. [27, 28,52], [67,78,79], [94,102,104] Random forest Random forest is an ensemble learning method. It contains multiple decision trees and integrates these decision trees to category of data.…”

Section: General Concepts Terminologies and Limitations Of Ai/mlmentioning

confidence: 99%

“…The size of trees and the number of variables usually determine the performance of model. [27,28,30], [47,48,59], [65][66][67]74,79], [90,99,101], [104,107,109] Support vector machine Support vector machine is usually used for classification and regression. It learns the optimal hyperplane to classify data.…”

Section: General Concepts Terminologies and Limitations Of Ai/mlmentioning

confidence: 99%

“…However, selecting the optimal kernel function is essential. [28,32,35], [52,59,[66][67][68], [75,78,79], [95,99,102], [103,104,108] Artificial neural network This is a kind of hierarchical nonlinear mapping network based on neurons and activation functions. Its structure includes three main parts, namely input layer, hidden layer and output layer.…”

Section: General Concepts Terminologies and Limitations Of Ai/mlmentioning

confidence: 99%

“…Decision tree is easy to understanding, but unstable and prone to overfitting. [47,49,60], [67,68,78], [79,97,100], [102] Lasso regression Lasso regression is a linear regression method using L1-regularization. L1-regularization can compress the coefficients of variables and change some coefficients to zero, so as to achieve the purpose of variable selection.…”

Section: Decision Treementioning

confidence: 99%

See 4 more Smart Citations

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

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Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

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Section: Ai/ml and Asthmamentioning

confidence: 99%