Missing data imputation on the 5-year survival prediction of breast cancer patients with unknown discrete values.
Breast cancer is the most frequently diagnosed cancer in women. Using historical patient information stored in clinical datasets, data mining and machine learning approaches can be applied to predict the survival of breast cancer patients. A common drawback is the absence of information, i.e., missing data, in certain clinical trials. However, most standard prediction methods are not able to handle incomplete samples and, then, missing data imputation is a widely applied approach for solving this inconvenience. Therefore, and taking into account the characteristics of each breast cancer dataset, it is required to perform a detailed analysis to determine the most appropriate imputation and prediction methods in each clinical environment. This research work analyzes a real breast cancer dataset from Institute Portuguese of Oncology of Porto with a high percentage of unknown categorical information (most clinical data of the patients are incomplete), which is a challenge in terms of complexity. Four scenarios are evaluated: (I) 5-year survival prediction without imputation and 5-year survival prediction from cleaned dataset with (II) Mode imputation, (III) Expectation-Maximization imputation and (IV) K-Nearest Neighbors imputation. Prediction models for breast cancer survivability are constructed using four different methods: K-Nearest Neighbors, Classification Trees, Logistic Regression and Support Vector Machines. Experiments are performed in a nested ten-fold cross-validation procedure and, according to the obtained results, the best results are provided by the K-Nearest Neighbors algorithm: more than 81% of accuracy and more than 0.78 of area under the Receiver Operator Characteristic curve, which constitutes very good results in this complex scenario.
Centro Universitario de la Defensa de San Javier (University Centre of Defence at the Spanish Air Force Academy), MDE-UPCT, Calle Coronel Lopez Peña, s/n, 30720 Santiago de la Ribera, Murcia, Spain. Electronic address: pedroj.garcia@cud.upct.es.
Pedro J
PJ
García-Laencina
Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal; Centre for Informatics and Systems, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal. Electronic address: pha@dei.uc.pt.
Pedro Henriques
PH
Abreu
Portuguese Institute of Oncology of Porto, Rua Dr. Antonio Bernardino de Almeida, 4200-072 Porto, Portugal. Electronic address: p_abreu@sapo.pt.
Miguel Henriques
MH
Abreu
Portuguese Institute of Oncology of Porto, Rua Dr. Antonio Bernardino de Almeida, 4200-072 Porto, Portugal. Electronic address: noemia.afonso@netcabo.pt.
Noémia
N
Afonoso
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000401:mortality*; D005260:Female; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D016019:Survival Analysis; D055815:Young Adult
5-year survival prediction; Breast cancer; Discrete data; Imputation; Missing data