Predictive Diagnosis through Data Mining for Cancer Detection and Treatment

Abstract

Cancer remains a major global health concern, with early detection and precise diagnosis being crucial for successful treatment outcomes. Data mining techniques offer immense potential for analysing complex cancer datasets and extracting valuable insights for predictive diagnosis. This research paper aims to investigate the application of data mining in cancer detection and treatment. The study will commence by assembling a comprehensive dataset comprising various cancer types, patient demographics, clinical characteristics, genomic data, and treatment outcomes. Data mining techniques, including clustering, classification, and association rule mining, will be employed to uncover hidden patterns and relationships within the data. Feature selection algorithms will be utilized to identify the most informative features for accurate cancer diagnosis and prognosis. Several predictive models, such as random forests, support vector machines, and deep learning networks, will be developed and compared based on performance metrics like sensitivity, specificity, accuracy, and area under the curve (AUC). The research will also explore the integration of multi-omics data, such as genomics, proteomics, and metabolomics, to enhance the predictive accuracy of cancer diagnosis and treatment response prediction. Additionally, the study will investigate the use of interpretability techniques to provide insights into the decision-making process of the predictive models. The findings of this research will contribute to the development of advanced predictive diagnostic systems for cancer. By leveraging data mining techniques, healthcare professionals can enhance their ability to detect cancer at early stages, predict treatment response, and recommend personalized treatment plans. Ultimately, this research aims to improve patient outcomes, optimize resource allocation, and guide the development of targeted therapies in the field of oncology.