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Machine Learning Diagnosis of Dengue Fever: A Cost-Effective Approach for Early Detection and Treatment

Author

Listed:
  • Hamzat Salami

    (Prince Abubakar Audu University, Anyigba, Faculty of Social Sciences, Nigeria University of Abuja, Faculty of Social Sciences, Nigeria)

  • Joy Eleojo Ebeh

    (Prince Abubakar Audu University, Anyigba, Faculty of Social Sciences, Nigeria University of Abuja, Faculty of Social Sciences, Nigeria)

  • Yakubu Ojo Aminu

    (Prince Abubakar Audu University, Anyigba, Faculty of Social Sciences, Nigeria University of Abuja, Faculty of Social Sciences, Nigeria)

Abstract

This research aims to explore the potential of machine learning algorithms for diagnosing of dengue fever and assess their cost-effectiveness compared to conventional methods. Four machine learning classifiers (K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, and Random Forest) were utilized. Feature selection and data balancing techniques were employed to enhance algorithm performance. The classifiers achieved high accuracy rates, with Naïve Bayes, Support Vector Machine, and Random Forest achieving 100% accuracy and K-Nearest Neighbor achieving 97.3% accuracy. Additionally, the cost-effectiveness analysis demonstrated that machine learning models for disease classification are the most cost-effective approach due to early detection and diagnosis, resulting in reduced healthcare costs. Therefore, it is recommended to promote the use of machine learning techniques in disease treatment for early detection and improved costeffectiveness.

Suggested Citation

  • Hamzat Salami & Joy Eleojo Ebeh & Yakubu Ojo Aminu, 2023. "Machine Learning Diagnosis of Dengue Fever: A Cost-Effective Approach for Early Detection and Treatment," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 229-238, August.
  • Handle: RePEc:ovi:oviste:v:xxiii:y:2023:i:1:p:229-238
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    References listed on IDEAS

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    2. Sunday O. Olatunji & Nawal Alsheikh & Lujain Alnajrani & Alhatoon Alanazy & Meshael Almusairii & Salam Alshammasi & Aisha Alansari & Rim Zaghdoud & Alaa Alahmadi & Mohammed Imran Basheer Ahmed & Moham, 2023. "Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
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    More about this item

    Keywords

    machine learning classifiers; dengue fever; cost-effectiveness;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I19 - Health, Education, and Welfare - - Health - - - Other

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