Optimized Feature Engineering and Machine Learning Approaches for Early Prediction of Renal Cell Carcinoma

Renal cancer is a fatal disease and ranks among the 14 common cancers in the latest WHO ranking. Background/Objectives Renal cell carcinoma (RCC) prediction not only diagnoses the disease at early stages but also prevents patients from suffering pain and eventual death. This study aimed to construct and validate a model that predicts renal cancer using machine learning (ML) classifiers. Methods Feature engineering techniques were used to refine the dataset, such as interaction feature generation, which categorizes the features according to values such as BMI, eGFR, and radiographic size. Binary clinical risk flags identify clinical risks according to the feature values, and correlation pruning removes the common and correlated features among various features. Three public datasets were collected, and the performance of seven machine learning classifiers was evaluated using the proposed model. The evaluation parameters included accuracy, precision, recall/sensitivity, specificity, F1 score, and Matthews Correlation Coefficient (MCC). An Explainable AI method was used to visualize the importance of the model characteristics. Results Among all the algorithms, the Multilevel Perceptron (MLP) algorithm fits best for the prediction of renal cancer with 98%, 96%, and 100% accuracy, and SHapley Additive exPlanations (SHAPs) are used to visualize the importance of all model features. Conclusions The proposed model provides an efficient method and shorter computation time to predict renal cancer using clinical features.