Two Staged Prediction of Gastric Cancer Patient’s Survival Via Machine Learning Techniques

@inproceedings{Liu2020TwoSP,
  title={Two Staged Prediction of Gastric Cancer Patient’s Survival Via Machine Learning Techniques},
  author={Peng Liu and Liuwen Li and Chenyang Yu and Shu-ming Fei},
  year={2020}
}
Cancer is one of the most common causes of death in the world, while gastric cancer has the highest incidence in Asia. Predicting gastric cancer patients’ survivability can inform patients care decisions and help doctors prescribe personalized medicine. Classification techniques have been widely used to predict survivability of cancer patients. However, very few attention has been paid to patients who cannot survive. In this research, we consider survival prediction to be a twostaged problem… Expand
Two-Stage Prediction of Comorbid Cancer Patient Survivability Based on Improved Infinite Feature Selection
TLDR
This study considers survival prediction to be a two-stage problem, and uses unsupervised infinite feature selection (UinfFS) to predict the five-year survivability of patients and the results indicate that the proposed method is effective. Expand

References

SHOWING 1-10 OF 23 REFERENCES
A tree ensemble-based two-stage model for advanced-stage colorectal cancer survival prediction
TLDR
The results show that the proposed classification approach can effectively handle the imbalanced survivability data, and the proposed regression method outperforms several state-of-the-art regression models. Expand
Comparision of Four Machine Learning Techniques for the Prediction of Prostate Cancer Survivability
TLDR
Artificial neural network has the best accuracy (85.64%) in predicting survivability of prostate cancer patients and several traditional machine learning techniques are applied to SEER database to classify mortality rate. Expand
Accuracy Enhanced Lung Cancer Prognosis for Improving Patient Survivability Using Proposed Gaussian Classifier System
TLDR
The Gaussian K-Base NB classifier is more effective than the existing machine learning algorithms for lung cancer prediction model and the proposed classification accuracy has measured using ROC methods. Expand
Robust predictive model for evaluating breast cancer survivability
TLDR
The main purpose of this work is to address the importance of the stability of a model and to suggest one of such models, which is a good candidate that medical professionals readily employ without consuming the time and effort for parameter searching. Expand
Persistence of data-driven knowledge to predict breast cancer survival
TLDR
The study concludes that data-driven knowledge obtained with machine learning methods must be subject to over time validation before it can be clinically and professionally applied. Expand
A dynamic gradient boosting machine using genetic optimizer for practical breast cancer prognosis
TLDR
The proposed GAOGB model demonstrates potential for practical incremental breast cancer prognosis, promising a combination of training effectiveness and efficiency, and validates the impact of parameter, adaptiveness and convergence in devising practical online learning algorithms. Expand
Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic
TLDR
This study aims at diagnosing and prognosticating breast cancer with a machine learning method based on random forest classifier and feature selection technique and states that this method can be used confidently for other breast cancer diagnosis problems, too. Expand
Breast cancer survivability via AdaBoost algorithms
TLDR
Data pre-processing RELIEF attributes selection, and Modest AdaBoost algorithms, are used to extract knowledge from the breast cancer survival databases in Thailand and computational results showed that Modest Ada boost outperforms Real and Gentle AdaBoosts. Expand
Predicting Survival of Patients with Spinal Ependymoma Using Machine Learning Algorithms with the SEER Database.
TLDR
It was reaffirmed that therapeutic factors, such as surgery and GTR, were associated with improved OS and ML techniques showed satisfactory results in predicting OS; however, the dataset was heterogeneous and complex with numerous missing values. Expand
Classifying Stage IV Lung Cancer From Health Care Claims: A Comparison of Multiple Analytic Approaches.
TLDR
Machine learning algorithms have potential to improve lung cancer stage classification but may be prone to overfitting, and degradation of accuracy between development and validation cohorts suggests the need for caution in implementing machine learning in research or care delivery. Expand
...
1
2
3
...