Predictive Modeling for Asthma Disease Detection: A Comparative Study of Machine Learning Algorithms
Year: 2024
Type: conference-paper
Abstract: Artificial Intelligence (AI) and Machine learning (ML) models have proven to be scalable approaches for handling several biomedical problems. Recent availability of high-quality datasets which captures various factors contributing to the respiratory disease, have enabled the development of robust models that deliver high accuracy and precision scores in early detection of respiratory diseases. Asthma is a common respiratory illness which causes shortness of breath, coughing, wheezing etc. This paper focuses on asthma disease detection. It makes two primary contributions: (1) an empirical evaluation of their performance on an asthma disease detection dataset with data mining and pre-processing techniques, and (2) the identification of the most effective approach for asthma disease detection based on rigorous evaluation using metrics such as precision, accuracy, recall, F-1, and F-beta scores. Cat Boost Classifier was found to be the best model which predicted asthma disease with a 96.04% accuracy.
Authors: Arka Mukherjee, Junali Jasmine Jena, Mahendra Kumar Gourisaria, Sricheta Parui, Sudhansu Shekhar Patra, Parthasarathi Pattnayak
Institution: KIIT University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4403407102