A Combinational Approach to Identify the Latent Disease clusters by using Machine Learning Techniques

• Published in: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS) Vol. 1; pp. 1418 - 1423
• Main Authors: Chandana, M, Suresh, K, Anusha, M
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19-03-2021
• Subjects: Accuracy; Bayes methods; Clustering algorithms; combinational approach; Communication systems; Forestry; Hierarchical clustering; Machine learning; Machine learning algorithms; Naive Bayesian; Random forest; Supervised; Supervised learning; Unsupervised
• ISBN: 9781665405201; 1665405201
• ISSN: 2575-7288
• DOI: 10.1109/ICACCS51430.2021.9441721

Latent disease is an infection illness which is not possible to reveal symptoms but resides health for a long period of time. In this work, proposed a novel model named a new approach for handling latent diseases by combining unsupervised and supervised learning techniques in machine learning. And it compare these algorithms by grouping the patients who are having similar characteristics of symptoms. That could be help for the primary and secondary purpose such as clinical research and treatment outcome improvements.