Current State of the Art for Survival Prediction in Cancer Using Data Mining Techniques

M.N., Doja; Kaur, Ishleen; Ahmad, Tanvir

doi:10.2174/1574893614666190902152142

Cited by 15 publications

(6 citation statements)

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“…But in the later stages, the survival rates vary from about 10% to 40%. Thus, the present study creates a model established on the cancer behavior (for advanced stage only) that will be more useful for clinicians in examining the survival of cancer patients [ 70 ]. It can be observed from Table 8 that almost all the studies used dataset of all the stages.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Kaur

Doja

Ahmad

et al. 2021

Computational Intelligence and Neuroscience

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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.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Kaur

Doja

Ahmad

et al. 2021

Computational Intelligence and Neuroscience

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“…Essentially, the cancer patient survival prediction model is a classification problem [23], including the screening of datasets and analyzing the connections between the data. So far, many data mining methods have been proposed in the literature to predict the survival status of esophageal cancer patients [24,25]. In [26], 90 breast cancer risk miRNAs are predicted based on the proposed DMTN by using the SVM classifier, which obtained an AUV of 0.9633.…”

Section: Introductionmentioning

confidence: 99%

Survival Prediction Model for Patients with Esophageal Squamous Cell Carcinoma Based on the Parameter-Optimized Deep Belief Network Using the Improved Archimedes Optimization Algorithm

Wang

Zhang

Sun

et al. 2022

Computational and Mathematical Methods in Medicine

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Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world. An effective survival prediction model can improve the quality of patients’ survival. Therefore, a parameter-optimized deep belief network based on the improved Archimedes optimization algorithm is proposed in this paper for the survival prediction of patients with ESCC. Firstly, a combination of features significantly associated with the survival of patients is found by the minimum redundancy and maximum relevancy (MRMR) algorithm. Secondly, a DBN network is introduced to make predictions for survival of patients. Aiming at the problem that the deep belief network model is affected by parameters in the construction process, this paper uses the Archimedes optimization algorithm to optimize the learning rate α and batch size β of DBN. In order to overcome the problem that AOA is prone to fall into local optimum and low search accuracy, an improved Archimedes optimization algorithm (IAOA) is proposed. On this basis, a survival prediction model for patients with ESCC is constructed. Finally, accuracy comparison tests are carried out on IAOA-DBN, AOA-DBN, SSA-DBN, PSO-DBN, BES-DBN, IAOA-SVM, and IAOA-BPNN models. The results show that the IAOA-DBN model can effectively predict the five-year survival rate of patients and provide a reference for the clinical judgment of patients with ESCC.

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“…The complexity and heterogeneity of cancer make it difficult to treat. Traditional clinical methods such as surgery, radiotherapy, and chemotherapy can be used to treat cancer, but the side effects of these methods are very obvious and can cause great discomfort for patients ( Doja et al, 2020 ). Although traditional anticancer drugs are effective, their shortcomings, such as gastrointestinal damage ( Mitchell, 2006 ), are also notable and can easily cause multidrug tumor resistance ( Holohan et al, 2013 ; Wijdeven et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation

et al. 2021

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Anticancer peptides (ACPs) have provided a promising perspective for cancer treatment, and the prediction of ACPs is very important for the discovery of new cancer treatment drugs. It is time consuming and expensive to use experimental methods to identify ACPs, so computational methods for ACP identification are urgently needed. There have been many effective computational methods, especially machine learning-based methods, proposed for such predictions. Most of the current machine learning methods try to find suitable features or design effective feature learning techniques to accurately represent ACPs. However, the performance of these methods can be further improved for cases with insufficient numbers of samples. In this article, we propose an ACP prediction model called ACP-DA (Data Augmentation), which uses data augmentation for insufficient samples to improve the prediction performance. In our method, to better exploit the information of peptide sequences, peptide sequences are represented by integrating binary profile features and AAindex features, and then the samples in the training set are augmented in the feature space. After data augmentation, the samples are used to train the machine learning model, which is used to predict ACPs. The performance of ACP-DA exceeds that of existing methods, and ACP-DA achieves better performance in the prediction of ACPs compared with a method without data augmentation. The proposed method is available at http://github.com/chenxgscuec/ACPDA.

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Current State of the Art for Survival Prediction in Cancer Using Data Mining Techniques

Cited by 15 publications

References 0 publications

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Survival Prediction Model for Patients with Esophageal Squamous Cell Carcinoma Based on the Parameter-Optimized Deep Belief Network Using the Improved Archimedes Optimization Algorithm

ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation

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