Machine Learning for Diabetes Detection in Females

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@article{1711240,
      author = {Aondofa Odot Addai},
      title = {Machine Learning for Diabetes Detection in Females},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {4},
      pages = {471-476},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711240.pdf},
      abstract = {Diabetes millitus commonly referred to as diabetes is a chronic medical deficiency characterized by abnormalities in the production and secretion of insulin in the human body system. Diabetes is a critical illness that can cause misfunctioning of several vital organs in the body system including the eyes, nerves, kidney, and the heart. The severity of symptoms can vary subject to the duration and type of diabetes. Individuals with high blood sugar levels particularly those with a complete lack of insulin such as children may experience symptoms such as increased appetite, polydipsia, weight loss, increased appetite, and vision problems. Diabetes affects approximately 9% of the entire adult population globally. Treatment of diabetes requires accurate and timely diagnosis. The use of Machine Learning techniques to support diabetes diagnosis has been widely adopted as an effective and efficient Artificial Intelligence approach in line with modern healthcare standards. Thus, this study presents the diabetes epidemic, prevalence, and diagnosis with emphasis on the use of Artificial Intelligence driven diagnosis. Different machine learning techniques are experimented on diabetes diagnosis for female patients particularly due to the greater risk of female patients to experience severe complications such as blindness from diabetic retinopathy and death from cardiovascular disease. A comparative analysis is performed for all experimented techniques to ascertain the optimal technique for diabetes diagnosis.},
      keywords = {Artificial Intelligence, Diabetes, Machine Learning},
      month = {October},
      doi = {https://doi.org/10.64388/IREV9I4-1711240-1397}
  }