An Integrated  Approach for Cancer Survival Prediction Using Data Mining Techniques

Abstract

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients’ survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing

“…The performance of our models, when evaluated on a never-before-seen internal holdout set, achieved accuracy, BAC, and AUC over 0.800, with our best models achieving AUC over 0.900. This performance was similar to or better than prior work seeking to predict survival of patients with cancer, 5,8,16,[34][35][36] despite using data that were more generalizable and readily available. Prior studies have predicted cancer survival for specific tumor sites such as breast or lung, or with the use of structured data such as processed clinical and genetic characteristics.…”

“… 3 Some models developed to date utilize structured data, that is, data that are processed into specific features such as the presence of genetic markers, demographics, or specific aspects of clinical history. 4 , 5 , 6 , 7 , 8 This may limit the widespread use of such models, as data availability varies among cancer treatment centers and between patients. It also limits what data can be used for a model, as not all clinical data are easily coded or categorized for extraction and analysis.…”

“…Predictive models trained by machine learning may allow more personalized predictions by using many features of a patient’s particular characteristics and disease, and have been shown to outperform the prediction of treating oncologists . Some models developed to date utilize structured data, that is, data that are processed into specific features such as the presence of genetic markers, demographics, or specific aspects of clinical history . This may limit the widespread use of such models, as data availability varies among cancer treatment centers and between patients.…”

Clinical Characteristics in the Prediction of Posttreatment Survival of Patients with Ovarian Cancer

“…With the development of big data techniques, data mining from available database has received more and more attention [ 11 , 12 ]. Therefore, an updated analysis with more available datasets is beneficial in discovery of more valuable predictive factors.…”

Body Weight Is a Valid Predictor of the Long-Term Prognosis of Cervical Cancer

“…The evolving development in big data techniques makes data mining from published database a feasible and affordable way for clinical studies [ 7 , 8 ], although the inconsistency in study design and retrospectively collected data make a lot of parameters incomparable between different studies [ 9 ]. Ideally, comprehensive parameters in the field of psychology and social-economy need to be obtained for a better understanding of factors influencing the prognosis of cervical cancer.…”