Trebuchet MSEarly identification of cancer indicators within clinical documentation is essential for improving diagnostic efficiency and patient outcomes. This study presents a Natural Language Processing (NLP) and machine learning framework designed to extract cancer-related indicators from unstructured clinical notes. Clinical text data obtained from Kaggle and structured diagnostic features from the Breast Cancer Wisconsin (Diagnostic) Dataset available through the UCI Machine Learning Repository were used to develop and evaluate the proposed model. The methodology involved comprehensive text preprocessing, TF–IDF-based feature extraction, and feature engineering to represent clinically meaningful patterns in narrative medical text. Multiple machine learning algorithms, including Logistic Regression, Support Vector Machines, Random Forest, and Gradient Boosting classifiers, were trained and evaluated using standard performance metrics. Experimental results indicate that ensemble learning approaches outperform traditional classifiers in detecting cancer-related information from clinical narratives. Among the evaluated models, the Gradient Boosting classifier achieved the best performance with an accuracy of 95%, precision of 94%, recall of 93%, and an F1-score of 0.93. These results demonstrate the effectiveness of machine learning–based NLP systems in identifying cancer indicators within electronic health records. The proposed framework highlights the potential of automated clinical text analysis to support early cancer detection, enhance clinical decision support systems, and improve healthcare data analytics.

Natural Language Processing, Machine Learning, Cancer Detection, Clinical Notes, Healthcare Analytics, Electronic Health Records, Text Mining.

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