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An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Kaur, Ishleen; Doja, M. N.; Ahmad, Tanvir; Ahmad, Musheer; Hussain, Amir; Nadeem, Ahmed; Abd El-Latif, Ahmed A.


Ishleen Kaur

M. N. Doja

Tanvir Ahmad

Musheer Ahmad

Ahmed Nadeem

Ahmed A. Abd El-Latif


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.

Journal Article Type Article
Acceptance Date Nov 27, 2021
Online Publication Date Dec 28, 2021
Publication Date 2021
Deposit Date Jan 20, 2022
Publicly Available Date Jan 20, 2022
Journal Computational Intelligence and Neuroscience
Print ISSN 1687-5265
Electronic ISSN 1687-5273
Publisher Hindawi
Peer Reviewed Peer Reviewed
Volume 2021
Article Number 6342226
Public URL


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