Computer Science

Permanent URI for this collectionhttps://etd.hu.edu.et/handle/123456789/76

Browse

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    INVESTIGATION OF MACHINE LEARNING MODELS FOR FOODBORNE DISEASE CLASSIFICATION
    (Hawassa University, 2024-11) WULETAWU IYASU FARACHO
    Foodborne disease is a disease that has a high prevalence in low and middle income countries around the world. There are many people affected by foodborne disease in Ethiopia, due to various causes. There are high burdens of infection; the control of most foodborne diseases in Ethiopia is in its infancy due to a lack of technology that can classify foodborne diseases easily in order to support healthcare professionals for better diagnoses. There is a lack of study conducted to classify the foodborne diseases which are common in Ethiopia. It is in view of this facts, the study aims to undertake an investigation on the topic and fill the research gap observed using machine learning model which can learn from past data, identify patterns and make decisions with a minimal human intervention. These applications in the healthcare and biomedical domain are popular for the early detection of diseases and help to make a better diagnosis. This study focuses on foodborne diseases, some of the prevalent foodborne illnesses in Ethiopia, selected in consultation with medical experts. To achieve the objective of the study the researcher used an experimental research design and mixed research approach (both quantitative and qualitative). For this study, secondary data of foodborne diseases were collected from Hospitals, and to perform most of the research activities such as data pre-processing, analysis, model training, and testing, python programming is used, and to design a conceptual model, Edraw max is used based on its good features. After preprocessing the collected data, the researcher trained a model using state-of- art machine learning algorithms like Decision Tree, Random Forest, XGBoost and Stacking ensemble learning method. Based on the experiment conducted, the Stacking ensemble learning method model outperforms the others with an accuracy of 98.1%, followed by Random Forest, XGBoost, and Decision Tree with accuracy of 97.5%, 96.9%, and 96.5% respectively. The result obtained by the study depicts that, the Stacking ensemble learning model is suitable for diseases classification.