A predictive model for the effective prognosis of Asthma using Asthma severity indicators

Pushpalatha, M.; R, Pooja M

doi:10.1109/iccci.2017.8117717

Cited by 5 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cluster analysis is the most applicable methodology at this stage. Supervised learning involves the application of classification algorithms with the intention of identifying subjects with the disease as well as predicting severity levels pertaining to the disease outcomes [3,4,5]. Commonly used classification algorithms including logistic regression, support vector machines and decision trees yield a considerably decent outcome [5,8], however the accuracies with which these algorithms perform will be better with the design of ensemble and hybrid techniques that utilize these algorithms in an efficient way [1,6].…”

Section: Methodsmentioning

confidence: 99%

Application of Learning Approaches in Healthcare

R¹

2021

IJAMST

View full text Add to dashboard Cite

The learning approaches in healthcare would aim at phenotyping the disease based on clinical as well as physiological characteristics as ideally disease is defined and diagnosed by a combination of clinical symptoms and physiologic abnormalities.The medicine today is advanced into new realm with the growth of applications of artificial intelligence and machine learning in healthcare. This is important as we will not be addressing the target population for a specific disease alone; rather predict the likely outcome of the related disease in an unknown population of interest with the knowledge gained. This is of utmost focus especially with rare diseases, the data for which are available in lower volumes. Further, prediction outcomes available at earlier stages are important to prepare points of care to handle disastrous outcomes resulting from the diseases.

show abstract

Section: Methodsmentioning

confidence: 99%

Application of Learning Approaches in Healthcare

R¹

2021

IJAMST

View full text Add to dashboard Cite

show abstract

Speech Quality Assessment in Wireless VoIP Communication Using Deep Belief Network

et al. 2018

View full text Add to dashboard Cite

Deployment of a Phenotypic Characterization System for Effective Identification of the Onset of Asthma Disease

Ravi,

Al Mazroa

et al. 2024

TOPHJ

View full text Add to dashboard Cite

Background Essentially, machine learning techniques help with clinical decision-making by forecasting prediction results based on recent and historical data, which are frequently found in carefully chosen clinical data repositories. In order to uncover hidden patterns in the data, machine learning applies sophisticated analytical techniques that conduct an exploratory analysis while constructing prediction models to support clinical judgment. Objective To effectively identify asthmatics in two distinct cohorts representing India's rural and urban populations by adopting a phenotypic characterization approach. Methods Cross-sectional and categorical in design, the data represent the two populations, with clinical history information emphasizing clinical symptoms and patterns defining the condition. The method adopts a hybrid approach since it uniquely blends the unsupervised and supervised learning techniques to explore the advantages of both. The clustering data emphasizing the phenotypic characteristics of asthma is input to the classifier, and the performance of the classifier was continuously monitored for significant improvement in the results. Results Asthma disease outcome predictions made by the hybrid decision support system were quite accurate, with classification accuracy reaching up to 85.1% and 95.3% for the two datasets, respectively. Conclusion Since asthma is a heterogeneous disease with multiple subtypes, employing clustering information in the form of cluster evaluation scores as an input parameter to the classifiers can effectively predict disease outcomes.

show abstract

A predictive model for the effective prognosis of Asthma using Asthma severity indicators

Cited by 5 publications

References 18 publications

Application of Learning Approaches in Healthcare

Application of Learning Approaches in Healthcare

Speech Quality Assessment in Wireless VoIP Communication Using Deep Belief Network

Deployment of a Phenotypic Characterization System for Effective Identification of the Onset of Asthma Disease

Contact Info

Product

Resources

About