Machine Learning in Diabetes Diagnosis: A Comprehensive Review
DOI:
https://doi.org/10.69968/ijisem.2024v3i480-85Keywords:
Diabetes detection, machine learning, Support Vector Machines, Gradient Boosting, ensemble methods, diagnostic accuracy, healthcareAbstract
Diabetes is a long-term condition that affects people all around the world. This review article looks at how machine learning might help find cases of diabetes earlier. The paper emphasises the efficacy of machine learning models in enhancing diagnostic accuracy and reliability by analysing several models, such as ensemble techniques, Support Vector Machines (SVM), and Gradient Boosting. Problems with data quality, making the models interpretable, and incorporating machine learning into clinical processes are some of the obstacles discussed in the article. Patient confidentiality and the possibility of prejudiced forecasts are among the ethical issues covered in the study. Machine learning might provide more accessible, efficient, and accurate ways to identify diabetes, according to the results, despite these obstacles. Ultimately, the paper stresses that in order to effectively use machine learning's advantages in healthcare, there must be continuous research, cooperation, and a thorough examination of practical and ethical concerns.
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