Application of machine learning techniques for predicting survival in ovarian cancer

Abstract

Background Ovarian cancer is the fifth leading cause of mortality among women in the United States. Ovarian cancer is also known as forgotten cancer or silent disease. The survival of ovarian cancer patients depends on several factors, including the treatment process and the prognosis. Methods The ovarian cancer patients’ dataset is compiled from the Surveillance, Epidemiology, and End Results (SEER) database. With the help of a clinician, the dataset is curated, and the most relevant features are selected. Pearson’s second coefficient of skewness test is used to evaluate the skewness of the dataset. Pearson correlation coefficient is also used to investigate the associations between features. Statistical test is utilized to evaluate the significance of the features. Six Machine Learning (ML) models, including K-Nearest Neighbors , Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), are implemented for survival prediction in both classification and regression approaches. An interpretable method, Shapley Additive Explanations (SHAP), is applied to clarify the decision-making process and determine the importance of each feature in prediction. Additionally, DTs of the RF model are displayed to show how the model predicts the survival intervals. Results Our results show that RF (Accuracy = 88.72%, AUC = 82.38%) and XGBoost (Root Mean Squad Error (RMSE)) = 20.61%, R2 = 0.4667) have the best performance for classification and regression approaches, respectively. Furthermore, using the SHAP method along with extracted DTs of the RF model, the most important features in the dataset are identified. Histologic type ICD-O-3, chemotherapy recode, year of diagnosis, age at diagnosis, tumor stage, and grade are the most important determinant factors in survival prediction. Conclusion To the best of our knowledge, our study is the first study that develops various ML models to predict ovarian cancer patients’ survival on the SEER database in both classification and regression approaches. These ML algorithms also achieve more accurate results and outperform statistical methods. Furthermore, our study is the first study to use the SHAP method to increase confidence and transparency of the proposed models’ prediction for clinicians. Moreover, our developed models, as an automated auxiliary tool, can help clinicians to have a better understanding of the estimated survival as well as important features that affect survival.

Screening ovarian cancer by using risk factors: machine learning assists

“…All ML algorithms obtained pleasant performance with more than 98% accuracy [ 41 ]. Also, in several studies, ML approaches have been leveraged to predict OC survival to give physicians better insight into the situation of OC patients [ 42 , 43 ]. Our study contribution is introducing preventive solutions through screening the high-risk groups of women concerning OC assisted with ML.…”

Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients

“…Thus, it is vital to educate clinicians and patients about the utility and interpretation of these models 41. Another concern about using ML models for predictive modeling is a lack of reproducibility due to bias brought about by missing data or small cohort sizes 42. Dihman et al 43▪ conducted a literature review to find 152 oncology studies from MEDLINE using ML models for predictive modeling and used a Prediction Model of Risk Assessment Tool to find that ~80% of studies used models with high-risk bias.…”

“…Thus, it is vital to educate clinicians and patients about the utility and interpretation of these models [41]. Another concern about using ML models for predictive modeling is a lack of reproducibility due to bias brought about by missing data or small cohort sizes [42].…”

Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning

“…The concept of SHAP value in game theory is introduced into the interpretation process of the ML model, which can not only reflect the influence of each sample feature, but also show the positivity and negativity of the influence of each feature on the prediction results. Its interpretability is verified in many models [ 9 , 10 ]. The trained model is subjected to tenfold cross-validation to test the performance of the model to reduce problems such as overfitting, and selection bias, and to give the generalization ability of the model on an independent dataset.…”