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Optimized Machine Learning Models for Hepatitis C Prediction: Leveraging Optuna for Hyperparameter Tuning and Streamlit for Model Deployment

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Pan-African Conference on Artificial Intelligence (PanAfriConAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2068))

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Abstract

Machine Learning techniques have gained significant attention for their potential to solve diverse real-world problems across various fields. This study uses machine learning algorithms to predict hepatitis C stages, a prevalent liver disease affecting a substantial portion of the global population. By employing a dataset encompassing 615 patients and incorporating a multitude of factors associated with hepatitis C, a comprehensive analysis was conducted to compare the performance of six prominent machine learning algorithms. The algorithms considered include categorical boosting (CatBoost), Gaussian Naive Bayes (GNB), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and ExtraTreeClassifier (ExtraT). To optimize the performance of these models, a hyperparameter optimization technique called Optuna was utilized to find the ideal parameters for each algorithm. Subsequently, all models’ performance was evaluated using the test dataset, comprising 20% of the overall patient data. The research findings revealed that the XGBoost algorithm emerged as the most effective approach, exhibiting a remarkable accuracy of 94.31%. Furthermore, the XGBoost model demonstrated exceptional F1-score, precision, and recall values, measuring 94.23%, 94.63%, and 94.31%, respectively. Building upon these promising results, we deployed the XGBoost model in a user-friendly web application leveraging Streamlit. This deployment ensures easy accessibility and usability of the model for the broader community.

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Author information

Authors and Affiliations

  1. African Institute for Mathematical Sciences Cameroon, Crystal Gardens Limbe 608 South West, Limbe, Cameroon

    Uriel Nguefack Yefou

  2. African Institute for Mathematical Sciences Senegal, Km2 route de Joal (IRD Center), Mbour 1418 Thies, Mbour, Senegal

    Pauline Ornela Megne Choudja, Binta Sow & Abduljaleel Adejumo

Authors
  1. Uriel Nguefack Yefou
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  2. Pauline Ornela Megne Choudja
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  3. Binta Sow
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  4. Abduljaleel Adejumo
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Corresponding author

Correspondence to Uriel Nguefack Yefou .

Editor information

Editors and Affiliations

  1. Ethiopian Artificial Intelligence Instit, Addis Adaba, Ethiopia

    Taye Girma Debelee

  2. HAWK University of Applied Sciences and Arts, Göttingen, Germany

    Achim Ibenthal

  3. Universität Ulm, Ulm, Germany

    Friedhelm Schwenker

  4. Ethiopian Artificial Intelligence Instit, Addis Ababa, Ethiopia

    Yehualashet Megersa Ayano

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Yefou, U.N., Choudja, P.O.M., Sow, B., Adejumo, A. (2024). Optimized Machine Learning Models for Hepatitis C Prediction: Leveraging Optuna for Hyperparameter Tuning and Streamlit for Model Deployment. In: Debelee, T.G., Ibenthal, A., Schwenker, F., Megersa Ayano, Y. (eds) Pan-African Conference on Artificial Intelligence. PanAfriConAI 2023. Communications in Computer and Information Science, vol 2068. Springer, Cham. https://doi.org/10.1007/978-3-031-57624-9_5

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  • DOI: https://doi.org/10.1007/978-3-031-57624-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57623-2

  • Online ISBN: 978-3-031-57624-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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