Application of machine learning techniques for predicting survival in ovarian cancer

BMC Medical Informatics and Decision Making

Research

Uffe Kock Wiil¹  Amin Naemi¹  Amir Sorayaie Azar²  Jamshid Bagherzadeh Mohasefi²  Samin Babaei Rikan²  Habibollah Pirnejad³  Matin Bagherzadeh Mohasefi⁴ 

[1] Center for Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark;Department of Computer Engineering, Urmia University, Urmia, Iran;Patient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, Iran;Erasmus School of Health Policy and Management (ESHPM), Erasmus University Rotterdam, Rotterdam, The Netherlands;School of Medicine, University of Bari-Aldo Moro, Bari, Italy;

关键词: Ovarian cancer;    Clinical features;    Survival prediction;    Machine learning;    Interpretable machine learning;   

DOI  :  10.1186/s12911-022-02087-y

 received in 2022-09-20, accepted in 2022-12-15,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundOvarian 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.MethodsThe 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.ResultsOur 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.ConclusionTo 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.

【 授权许可】

CC BY   
© The Author(s) 2022

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