An Artificial intelligence technique for the prediction of persistent asthma in children

Chatzimichail, E.; Rigas, A. G.; Paraskakis, Emmanouil

doi:10.1109/itab.2010.5687810

Cited by 19 publications

(19 citation statements)

References 16 publications

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“…The prediction is considered to be true positive (TP) if the patient has asthma and it is correctly predicted as asthmatic. On the contrary, if the asthmatic patient is incorrectly predicted as nonasthmatic, the prediction is assigned as false negative (FN) [30]. False positive (FP) and true negative (TN) predictions can be determined in the same way.…”

Section: Resultsmentioning

confidence: 99%

An Intelligent System Approach for Asthma Prediction in Symptomatic Preschool Children

Chatzimichail

Paraskakis

Sitzimi

et al. 2013

Computational and Mathematical Methods in Medicine

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Objectives. In this study a new method for asthma outcome prediction, which is based on Principal Component Analysis and Least Square Support Vector Machine Classifier, is presented. Most of the asthma cases appear during the first years of life. Thus, the early identification of young children being at high risk of developing persistent symptoms of the disease throughout childhood is an important public health priority. Methods. The proposed intelligent system consists of three stages. At the first stage, Principal Component Analysis is used for feature extraction and dimension reduction. At the second stage, the pattern classification is achieved by using Least Square Support Vector Machine Classifier. Finally, at the third stage the performance evaluation of the system is estimated by using classification accuracy and 10-fold cross-validation. Results. The proposed prediction system can be used in asthma outcome prediction with 95.54 % success as shown in the experimental results. Conclusions. This study indicates that the proposed system is a potentially useful decision support tool for predicting asthma outcome and that some risk factors enhance its predictive ability.

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Section: Resultsmentioning

confidence: 99%

An Intelligent System Approach for Asthma Prediction in Symptomatic Preschool Children

Chatzimichail

Paraskakis

Sitzimi

et al. 2013

Computational and Mathematical Methods in Medicine

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“…There is a need for using the artificial intelligence and machine learning to overcome this disease. Researchers in (Chatzimichail et al, 2010) proposed a technique that will help in the diagnostic medical field. They have used the artificial neural network to diagnose the asthma.…”

Section: Resultsmentioning

confidence: 99%

Use of AI and Machine Learning for Asthma Patients: A Systematic Literature Review

Fadziso

Choi²

2020

Malays. j. med. biol. res.

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In modern times, the collection of data is not a big deal but using it in a meaningful is a challenging task. Different organizations are using artificial intelligence and machine learning for collecting and utilizing the data. These should also be used in the medical because different disease requires the prediction. One of these diseases is asthma that is continuously increasing and affecting more and more people. The major issue is that it is difficult to diagnose in children. Machine learning algorithms can help in diagnosing it early so that the doctors can start the treatment early. Machine learning algorithms can perform this prediction so this study will be helpful for both the doctors and patients. There are different machine learning predictive algorithms are available that have been used for this purpose.

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“…Six of 24 studies target children with wheezing or coughing symptoms [ 17 , 88 , 98 , 100 , 101 , 111 ]. Sixteen models used predictors collected from a (parental) questionnaire [ 89 , 90 , 94 – 98 , 100 – 104 , 106 – 110 ] and family history [ 88 – 91 , 94 – 103 , 107 ]. Three models used genetic information [ 92 , 101 , 105 ].…”

Section: Resultsmentioning

confidence: 99%

“…It is unclear how the model would perform in the general child population, where the prevalence of future asthma development remains unmodified. Chatzimichail et al performed five studies and built one machine learning model per study [ 106 – 110 ]. Each study used many candidate predictors and built a model achieving an accuracy ≥95 %.…”

Section: Resultsmentioning

confidence: 99%

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A systematic review of predictive models for asthma development in children

Luo

Nkoy

Stone

et al. 2015

BMC Med Inform Decis Mak

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BackgroundAsthma is the most common pediatric chronic disease affecting 9.6 % of American children. Delay in asthma diagnosis is prevalent, resulting in suboptimal asthma management. To help avoid delay in asthma diagnosis and advance asthma prevention research, researchers have proposed various models to predict asthma development in children. This paper reviews these models.MethodsA systematic review was conducted through searching in PubMed, EMBASE, CINAHL, Scopus, the Cochrane Library, the ACM Digital Library, IEEE Xplore, and OpenGrey up to June 3, 2015. The literature on predictive models for asthma development in children was retrieved, with search results limited to human subjects and children (birth to 18 years). Two independent reviewers screened the literature, performed data extraction, and assessed article quality.ResultsThe literature search returned 13,101 references in total. After manual review, 32 of these references were determined to be relevant and are discussed in the paper. We identify several limitations of existing predictive models for asthma development in children, and provide preliminary thoughts on how to address these limitations.ConclusionsExisting predictive models for asthma development in children have inadequate accuracy. Efforts to improve these models’ performance are needed, but are limited by a lack of a gold standard for asthma development in children.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0224-9) contains supplementary material, which is available to authorized users.

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An Artificial intelligence technique for the prediction of persistent asthma in children

Cited by 19 publications

References 16 publications

An Intelligent System Approach for Asthma Prediction in Symptomatic Preschool Children

An Intelligent System Approach for Asthma Prediction in Symptomatic Preschool Children

Use of AI and Machine Learning for Asthma Patients: A Systematic Literature Review

A systematic review of predictive models for asthma development in children

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