Zobrazit v EDS

Predictive Modeling of Cancer in Multifactorial Diseases Using Enhanced Cancer-Onset Algorithm.

Uloženo v:
Podrobná bibliografie
Název: Predictive Modeling of Cancer in Multifactorial Diseases Using Enhanced Cancer-Onset Algorithm.
Autoři: Sreedhar, B., Haritha, T., Babu, S. Dilli, Geetha, D., Kumar, M. Sunil, Ganesh, D.
Zdroj: Frontiers in Health Informatics; 2024, Vol. 13 Issue 3, p8600-8610, 11p
Témata: CHRONIC kidney failure, ECOSYSTEM dynamics, ECOLOGICAL disturbances, HEART failure, PREDICTION models
Abstrakt: In recent years, the realization has increased that multifactorial diseases are involved not only in cancer progression but also in its onset. Predicting cancer onset is difficult for traditional approaches when we have so many interacting contributors like heart failure, chronic kidney disease, and metabolic liver diseases. In this paper, a new method inspired by nature called the Enhanced Cancer-Onset Algorithm (ECOA) is introduced that uses ecosystem dynamics for these intricate relationships to provide an accurate prediction of cancer initiation. An ECOA model exhibited 92 % accuracy, as confirmed by the validation studies using a cohort dataset for better performance than other traditional machine learning methods. The novelty of ECOA model as opposed to a compartmental transmission structure is SEIR or Stochastic ache detection models. This flexible part of modelling dynamic interactions and disease representation for disease calm down in response to an external stressor. So, it can be considered as differences at using single prediction comfort base on incorporate essential properties into more holistic predictor. The social behaviours regulatory parameter reduces effect limit likelihood spreading catch up ability. [ABSTRACT FROM AUTHOR]
Copyright of Frontiers in Health Informatics is the property of Iranian Journal of Medical Informatics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Biomedical Index
Popis
Abstrakt:In recent years, the realization has increased that multifactorial diseases are involved not only in cancer progression but also in its onset. Predicting cancer onset is difficult for traditional approaches when we have so many interacting contributors like heart failure, chronic kidney disease, and metabolic liver diseases. In this paper, a new method inspired by nature called the Enhanced Cancer-Onset Algorithm (ECOA) is introduced that uses ecosystem dynamics for these intricate relationships to provide an accurate prediction of cancer initiation. An ECOA model exhibited 92 % accuracy, as confirmed by the validation studies using a cohort dataset for better performance than other traditional machine learning methods. The novelty of ECOA model as opposed to a compartmental transmission structure is SEIR or Stochastic ache detection models. This flexible part of modelling dynamic interactions and disease representation for disease calm down in response to an external stressor. So, it can be considered as differences at using single prediction comfort base on incorporate essential properties into more holistic predictor. The social behaviours regulatory parameter reduces effect limit likelihood spreading catch up ability. [ABSTRACT FROM AUTHOR]
ISSN:26767104