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

Area of Science:

• Oncology
• Bioinformatics
• Artificial Intelligence

Background:

• Early diagnosis and accurate genetic mutation prediction are crucial for urinary bladder cancer management.
• Identifying biomarkers for prognosis and therapy is essential in cancer care.
• Genetic mutations play a significant role in bladder cancer development and progression.

Purpose of the Study:

• To develop an AI-based predictive model for detecting mutation types in urinary bladder cancer.
• To improve the accuracy and reliability of mutation prediction for diagnostic and prognostic purposes.
• To explore the potential of artificial intelligence in identifying mutational landscapes for therapeutic strategies.

Main Methods:

• Collected mutation data from public human bladder cancer cell line datasets.
• Designed a multilevel ensemble model integrating Artificial Neural Network (ANN) and Random Forest (RF) classifier.
• Trained and validated the model on DNA sequence data to predict base pair insertions and deletions, comparing it with SVM, naive Bayes, and decision trees.

Main Results:

• The proposed ensemble model achieved nearly 95% prediction accuracy for mutation types.
• The AI model significantly outperformed traditional machine learning approaches like SVM and naive Bayes.
• Integration of ANN and RF enhanced classification robustness and reliability in mutation prediction.

Conclusions:

• AI techniques show significant potential for early mutation detection in urinary bladder cancer.
• The developed model's high accuracy can aid in identifying diagnostic and prognostic mutational landscapes.
• This approach offers promise for guiding future therapeutic strategies in bladder cancer treatment.