A multilevel ensemble model for predicting mutation types in bladder cancer.

BACKGROUND

In urinary bladder cancer, early diagnosis plays a key role for effective patient management. Moreover, accurate prediction of genetic mutations plays a critical role in identifying biomarkers for prognosis and therapy.

METHODS

Mutation types and other details were collected from publicly available datasets of human bladder cancer cell lines. A multilevel ensemble model was designed by integrating an Artificial Neural Network (ANN) with a Random Forest (RF) classifier. The model was trained and validated on DNA sequence data to predict mutation types, specifically base pair insertions and deletions. Performance was compared against traditional machine learning models including SVM, naive Bayes, and decision trees.

RESULTS

The proposed ensemble model achieved nearly 95% prediction accuracy, significantly outperforming conventional approaches. The integration of ANN and RF improved classification robustness and reliability in mutation type prediction. This study presents the development of a predictive model for mutation detection in bladder cancer using an AI-based approach.

CONCLUSION

This novel study highlights the potential of AI techniques for early detection of mutations in urinary bladder cancer. The highly accurate prediction capability of the developed model offers promise for identifying diagnostic and prognostic mutational landscapes to guide future therapeutic strategies.