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Computer Aided Medical Diagnosis System Using Logistics Regression

AKINWOLE Agnes Kikelomo, YEKINI Nureni Asafe

2026enmachine learningmalaria diagnosislogistic regressionmedical diagnosishealthcare

Abstract

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This work focused on the designing of a medical diagnosis system using Supervised Machine Learning. Logistics Regression Algorithms (LRA) was adopted, where the label inputs for the dataset, which included symptoms, were trained and mapped with the input from the user. The diagnosis of malaria was considered in this study; the system verified the efficacy of logistic regression in the medical decision support framework. Medical practitioners and health workers can utilize this system to enhance their decision-making processes for malaria diagnosis. The implementation of this system aims to alleviate the challenges associated with diagnosing malaria in patients and to minimize congestion in hospitals. This research indicates that the application of machine learning in medical diagnostics can improve efficiency and accuracy in health care settings, providing timely support and reducing the workload on medical staff. The findings emphasize the importance of integrating technology and artificial intelligence in medical diagnosis to facilitate better healthcare outcomes.

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Cite This Work

@article{d3803b71-6e8f-44b1-8a6c-14259467d352,
  title={Computer Aided Medical Diagnosis System Using Logistics Regression },
  author={AKINWOLE Agnes Kikelomo and YEKINI Nureni Asafe},
  year={2026},
  language={en}
}
TY  - JOUR
TI  - Computer Aided Medical Diagnosis System Using Logistics Regression 
AU  - AKINWOLE Agnes Kikelomo
AU  - YEKINI Nureni Asafe
PY  - 2026
LA  - en
ER  -

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