Innovations and Interdisciplinary Solutions for Underserved Areas. Third EAI International Conference, InterSol 2019, Cairo, Egypt, February 14–15, 2019, Proceedings

Research Article

Towards an Efficient Prediction Model of Malaria Cases in Senegal

  • @INPROCEEDINGS{10.1007/978-3-030-34863-2_15,
        author={Ousseynou Mbaye and Mouhamadou Ba and Gaoussou Camara and Alassane Sy and Balla Mboup and Aldiouma Diallo},
        title={Towards an Efficient Prediction Model of Malaria Cases in Senegal},
        proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. Third EAI International Conference, InterSol 2019, Cairo, Egypt, February 14--15, 2019, Proceedings},
        proceedings_a={INTERSOL},
        year={2019},
        month={11},
        keywords={Malaria Diagnosis Data imputation Prediction model},
        doi={10.1007/978-3-030-34863-2_15}
    }
    
  • Ousseynou Mbaye
    Mouhamadou Ba
    Gaoussou Camara
    Alassane Sy
    Balla Mboup
    Aldiouma Diallo
    Year: 2019
    Towards an Efficient Prediction Model of Malaria Cases in Senegal
    INTERSOL
    Springer
    DOI: 10.1007/978-3-030-34863-2_15
Ousseynou Mbaye1,*, Mouhamadou Ba1,*, Gaoussou Camara,*, Alassane Sy1,*, Balla Mboup2,*, Aldiouma Diallo3,*
  • 1: LIMA,Université Alioune Diop
  • 2: Région Médicale de Diourbel
  • 3: Vitrome, IRD. campus universitaire Hann maristes
*Contact email: ousseynou.mbaye@uadb.edu.sn, mouhamadoulamine.ba@uadb.edu.sn, gaoussou.camara@uadb.edu.sn, alassane.sy@uadb.edu.sn, bmmboup@yahoo.fr, aldiouma.diallo@ird.fr

Abstract

One amongst the most deadly diseases in the world, Malaria remains a real flail in Sub-saharan Africa. In underdeveloped countries, e.g. Senegal, such a situation is acute due to the lack of high quality healthcare services and well-formed persons able to perform accurate diagnosis of diseases that patients suffer from. This requires to set up automated tools which will help medical actors in their decision making process. In this paper, we present first steps towards an efficient way to automatically diagnosis an occurence or not of Malaria based on patient signs and symptoms, and the outcome from the quick diagnosis test. Our prediction approach is built on the logistic regression function. First experiments on a real world patient dataset collected in Senegal, as well as a semi-synthetic dataset, show promising performance results regarding the effectiveness of the proposed approach.