Mathematical modeling and machine learning for public health decision-making: the case of breast cancer in Benin

Agossou, Cyrille; Atchadé, Mintodê Nicodème; Djibril, Aliou Moussa; Kurisheva, Svetlana Vladimirovna

doi:10.3934/mbe.2022080

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…This was observed in different fields including the medical one (e.g. [23], [24], [25]). Indeed, in the medical and health-care field, datasets are increasingly involved in planning and managing limited resources and to assist the decision-making process.…”

Section: Discussionmentioning

confidence: 83%

New Real Dataset Creation to Develop an Intelligent System for Predicting Chemotherapy Protocols

BRAHIM,

BENLLARCH,

BENHIMA

et al. 2023

IJACSA

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Breast cancer is the most common cancer diagnosed in women. In developing countries, controlling this scourge is often problematic due to late diagnosis and the lack of medical and human resources. Automation and optimization of treatment is then needed to improve patient outcome. The use of medical datasets could, according to medical staff and pharmacists, assist them in clinical decision-making and would allow for better use of resources especially when limited. In our paper, a new real dataset was produced by collecting medical and personal data from 601 patients with breast cancer at the University Hospital Center (UHC) Mohammed VI of Marrakech. Data of women diagnosed with breast cancer from January 2018 at UHC were assessed. Most patients were 24-85 year-old, with an average age of 48.84 years. Patient age, performance status (PS), cancer stage and subtype, treatment patterns and correlations among the different variables were analyzed. The created dataset will help to determine the most appropriate treatment regimen depending on the individual characteristics of patients to allow for better use of limited resources.

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Section: Discussionmentioning

confidence: 83%

New Real Dataset Creation to Develop an Intelligent System for Predicting Chemotherapy Protocols

BRAHIM,

BENLLARCH,

BENHIMA

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…The work in [425] involves short-term electricity generation forecasting using machine learning algorithms, with a case study of the Benin Electricity Community (CEB). The work in [426] utilizes mathematical modeling and machine learning for public health decision-making, with a focus on the case of breast cancer in Benin. The work in [427] maps suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling.…”

Section: H Republic Of Thementioning

confidence: 99%