2024, Vol. 5, Issue 2, Part A

Liver cancer is a severe global health problem, and accurate prediction models are essential for early detection and effective treatment. In this research article, we propose an innovative approach for liver cancer prediction by addressing the challenges of outliers and over fitting. By integrating anomaly detection techniques and ensemble learning within a framework of neural network clustering and optimal tuning, our model demonstrates improved accuracy and robustness in predicting liver cancer.

The proposed model consists of four main steps. Data pre-processing involves identifying and removing outliers, selecting the most relevant features, and balancing the dataset. Neural network clustering uses K-Means clustering to identify distinct groups of patients based on their features. The cluster labels are then encoded as features and added to the dataset. Ensemble learning uses Gradient Boosting to build a predictive model for liver cancer. The predictions from multiple Gradient Boosting models are aggregated using majority voting. Hyper-parameter optimization uses Bayesian optimization to fine-tune the hyper- parameters of the model.

The proposed model was evaluated on a dataset of 10,000 patients. The results showed that the model achieved an accuracy of 93.2% on the test set. This is a significant improvement over the accuracy of previous models, which have typically ranged from 85% to 90%. The proposed model also showed improved robustness to outliers and over fitting.

The proposed study presents a novel approach for liver cancer prediction that shows promise for improving the accuracy and generalizability of liver cancer prediction models. The proposed model achieved an accuracy of 93.2% on the test set, which is a significant improvement over the accuracy of previous models. The proposed model also showed improved robustness to outliers and over fitting.

The proposed model is a promising approach for liver cancer prediction. It is more accurate and robust than previous models, and it can be used to improve the early detection and treatment of liver cancer.