Integrating Fuzzy C-Means and DBSCAN: A Hybrid Approach to Medical Data Mining

Medical data mining is crucial to gain meaningful insights from complex healthcare databases. Medical data sometimes exhibits ambiguity and overlap due to inconsistent diagnosis and varying patient situations. This work proposes a hybrid clustering strategy that combines Fuzzy C-Means (FCM) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to tackle these challenges. FCM's capacity to manage fuzzy memberships allows each data point to belong to many clusters with varying degrees of membership, accommodating the inherent ambiguities in medical data. With the help of the density-based clustering algorithm, the model can better identify and manage noise while detecting clusters with varying densities and shapes. The integration of the hybrid model aims to enhance patient segmentation by facilitating the identification of more complex and significant subgroups based on clinical markers. This technique improves the precision of sickness classification, leading to more customized treatment plans. Experimental validation and case studies show significant improvements in clustering quality of the model over existing methods. This leads to data that are more easily comprehended by medical professionals. The results show how the hybrid model might be a helpful tool for decision support systems and precision medicine, which would assist medical practitioners.