Optimized Feature Engineering and Machine Learning Approaches for Early Prediction of Renal Cell Carcinoma
- 1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune 412115, India
- 2Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
Abstract
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.
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BibTeX
@article{chankhore2026optimized,
title = {{Optimized Feature Engineering and Machine Learning Approaches for Early Prediction of Renal Cell Carcinoma}},
author = {Neha Chankhore and Deepali Vora and Paolo Barsocchi and Akash Kumar Bhoi},
journal = {Cancers},
year = {2026},
month = oct,
doi = {10.3390/cancers18203384},
url = {https://www.mdpi.com/2072-6694/18/20/3384},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Optimized Feature Engineering and Machine Learning Approaches for Early Prediction of Renal Cell Carcinoma
AU - Neha Chankhore
AU - Deepali Vora
AU - Paolo Barsocchi
AU - Akash Kumar Bhoi
PY - 2026
DA - 2026/10/01
JO - Cancers
DO - 10.3390/cancers18203384
UR - https://www.mdpi.com/2072-6694/18/20/3384
AB - 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.
SN - 2072-6694
ER -