Data mining in cancer research involves several steps:
Data Collection: Gathering data from various sources such as patient records, clinical trials, and genomic databases. Data Preprocessing: Cleaning and transforming the collected data to make it suitable for analysis. Data Analysis: Applying data mining techniques to discover patterns, correlations, and knowledge. Model Construction: Building predictive models that can be used for diagnosis, prognosis, and treatment optimization. Validation and Testing: Ensuring the reliability and accuracy of the models using separate datasets or cross-validation techniques.
