Customized Machine Learning Techniques for Enhancing Cancer Detection Through Advanced Cancerous Data Analysis

Early cancer identification treatment results are greatly improved by currently conventional approaches, which frequently have limitations including subjectivity and inefficiency. Machine learning provides a data-driven strategy to overcome these constraints by examining clinical and genomic data. Several machine learning models are assessed using the Clinical and Genomic Dataset of Cancer Patients which includes both clinical and genomic characteristics, in the proposed technique. Random Forest Classifier (RFC), K-Nearest Neighbours (KNN), K-Means, Support Vector Machine (SVM), and Decision Tree are among the techniques that have been tested. The F1 Score and Log Loss measurements are used to evaluate performance. In conclusion, SVM provides robust and accurate cancer diagnosis, making it the most dependable model for this dataset. These results highlight how machine learning can effectively use clinical and genetic data to transform early cancer detection.