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– ABSTRACT:
Introduction: In the realm of healthcare, the integration of machine learning (ML) has become increasingly pivotal, particularly in addressing the challenges posed by diabetes. So This study endeavors to explore and develop ML models tailored for diagnosing diabetic patients and predicting optimal insulin dosage.
Diagnosis Enhancement: The first facet of our investigation focuses on leveraging ML algorithms to enhance the early diagnosis of diabetes. Our models utilize varied patient data, employing advanced algorithms for a nuanced understanding, enabling early disease identification and intervention.
Predicting Insulin Dosage: A critical aspect of diabetes management lies in both predicting and optimizing insulin dosage. Our ML models delve into comprehensive datasets, encompassing variables such as blood glucose levels, dietary patterns, and physical activity. Through advanced algorithms, these models forecast insulin requirements with remarkable precision, ensuring personalized and effective treatment strategies.
Feature Extraction and Selection: Extracting key features refines model interpretability, identifying variables crucial for diabetes diagnosis and insulin prediction, enhancing the ability to discern relevant patterns.
Algorithmic Diversity and Training: This study critically evaluates following spectrum of ML algorithms, including decision trees, support vector machines, and neural networks. Rigorous training and validation processes refine the models’ adaptability to the dynamic nature of diabetic patient data. The careful selection of algorithms ensures robust and consistent performance across diverse patient populations.
Performance Evaluation: Comprehensive metrics, including sensitivity, specificity, and precision, are employed to rigorously assess the efficacy of our ML models. The validation process encompasses diverse datasets, guaranteeing the generalizability of our models across varied demographics and populations.
Conclusion: In conclusion, our research underscores the transformative potential of ML models in diabetes diagnosis and insulin dosage prediction. Seamless integration of advanced algorithms with comprehensive datasets promises a paradigm shift, revolutionizing diabetes care for proactive, personalized treatment.
