Data-Driven Approaches to Cancer Incidence Classification: Mining Health Data from Bhopal Gas Tragedy - Gour, Sanjeev; Randa, Rajendra (Mathematics and Computer Science: Contemporary Developments Vol. 10, 2024)
Cancer remains one of the most formidable health challenges globally, and India has seen a steady rise in cancer incidence rates over the years. The ability to effectively analyze large-scale cancer datasets is crucial for understanding disease patterns, improving diagnosis, and guiding public health interventions. In this research, advanced data mining techniques were leveraged, specifically classification and clustering, to examine cancer incidence patterns in the aftermath of the Bhopal Gas Tragedy, a catastrophic industrial disaster. Our study focuses on comparing the incidence rates of Tobacco-Related Cancer (TCR) and Non-Tobacco-Related Cancer (Non-TCR) in two distinct regions of Bhopal, which were partitioned after the tragedy. Using over 40 years of data from the Population-Based Cancer Registry (PBCR) of Bhopal, data mining methodologies were applied to uncover hidden patterns and correlations within the cancer incidence data. The study seeks to explore the long-term impact of environmental exposure on cancer prevalence, particularly the difference in cancer types between the two regions. By employing the WEKA tool, a well-established platform for machine learning and data mining, cancer cases were systematically classified and significant insights were extracted from the data. Our findings reveal notable differences in cancer incidence between the two regions, offering insights into how environmental factors, lifestyle choices, and socio-economic conditions may influence cancer development. The study highlights the value of data-driven approaches in health care, particularly as a decision support system for medical analysts. These insights not only contribute to the understanding of cancer epidemiology in Bhopal but also underscore the importance of continuous health monitoring in populations affected by industrial disasters. Furthermore, the methodology applied in this study serves as a foundation for future research aimed at improving cancer prevention, early detection, and personalized treatment strategies in similar contexts.