Holistic Integration of Clustering Algorithms for Improved Diabetes Prediction
Keywords:
Diabetes detection, Machine learning, K-Means, Ensemble learning, Healthcare.Abstract
This research paper presents a ground-breaking method aimed at refining the precision and dependability of diabetes risk prediction models. By merging K-Means, Fuzzy C-Means (FCM), and Hierarchical Clustering algorithms, the study tackles the intricacies of diabetes-related data within the PIMA Indian Diabetes dataset. Extensive data pre-processing, encompassing tasks such as managing missing values and standardizing features, lays the foundation for this integrated clustering approach. Through meticulous evaluation, which includes comparisons with individual clustering methods and conventional prediction models, the study demonstrates a notable enhancement in accuracy and resilience. These outcomes highlight the importance of amalgamating diverse clustering techniques in healthcare analytics, fostering a nuanced comprehension of patient data and enabling early detection of diabetes risk. The research underscores the significance of adopting a comprehensive clustering strategy to advance predictive modelling in diabetes risk assessment, offering valuable insights to the field.
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