• Title, Summary, Keyword: Cancer prediction

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Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data

  • Jeong, Seokho;Mok, Lydia;Kim, Se Ik;Ahn, TaeJin;Song, Yong-Sang;Park, Taesung
    • Genomics & Informatics
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    • v.16 no.4
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    • pp.32.1-32.7
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    • 2018
  • Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

Cancer Prediction Based on Radical Basis Function Neural Network with Particle Swarm Optimization

  • Yan, Xiao-Bo;Xiong, Wei-Qing;Hu, Liang;Zhao, Kuo
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.18
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    • pp.7775-7780
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    • 2014
  • This paper addresses cancer prediction based on radial basis function neural network optimized by particle swarm optimization. Today, cancer hazard to people is increasing, and it is often difficult to cure cancer. The occurrence of cancer can be predicted by the method of the computer so that people can take timely and effective measures to prevent the occurrence of cancer. In this paper, the occurrence of cancer is predicted by the means of Radial Basis Function Neural Network Optimized by Particle Swarm Optimization. The neural network parameters to be optimized include the weight vector between network hidden layer and output layer, and the threshold of output layer neurons. The experimental data were obtained from the Wisconsin breast cancer database. A total of 12 experiments were done by setting 12 different sets of experimental result reliability. The findings show that the method can improve the accuracy, reliability and stability of cancer prediction greatly and effectively.

Early Detection of Lung Cancer Risk Using Data Mining

  • Ahmed, Kawsar;Abdullah-Al-Emran, Abdullah-Al-Emran;Jesmin, Tasnuba;Mukti, Roushney Fatima;Rahman, Md. Zamilur;Ahmed, Farzana
    • Asian Pacific Journal of Cancer Prevention
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    • v.14 no.1
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    • pp.595-598
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    • 2013
  • Background: Lung cancer is the leading cause of cancer death worldwide Therefore, identification of genetic as well as environmental factors is very important in developing novel methods of lung cancer prevention. However, this is a multi-layered problem. Therefore a lung cancer risk prediction system is here proposed which is easy, cost effective and time saving. Materials and Methods: Initially 400 cancer and non-cancer patients' data were collected from different diagnostic centres, pre-processed and clustered using a K-means clustering algorithm for identifying relevant and non-relevant data. Next significant frequent patterns are discovered using AprioriTid and a decision tree algorithm. Results: Finally using the significant pattern prediction tools for a lung cancer prediction system were developed. This lung cancer risk prediction system should prove helpful in detection of a person's predisposition for lung cancer. Conclusions: Most of people of Bangladesh do not even know they have lung cancer and the majority of cases are diagnosed at late stages when cure is impossible. Therefore early prediction of lung cancer should play a pivotal role in the diagnosis process and for an effective preventive strategy.

Lung Cancer Risk Prediction Method Based on Feature Selection and Artificial Neural Network

  • Xie, Nan-Nan;Hu, Liang;Li, Tai-Hui
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.23
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    • pp.10539-10542
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    • 2015
  • A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

A Hybrid Mod K-Means Clustering with Mod SVM Algorithm to Enhance the Cancer Prediction

  • Kumar, Rethina;Ganapathy, Gopinath;Kang, Jeong-Jin
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.2
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    • pp.231-243
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    • 2021
  • In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

Artificial Neural Network for Prediction of Distant Metastasis in Colorectal Cancer

  • Biglarian, Akbar;Bakhshi, Enayatollah;Gohari, Mahmood Reza;Khodabakhshi, Reza
    • Asian Pacific Journal of Cancer Prevention
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    • v.13 no.3
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    • pp.927-930
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    • 2012
  • Background and Objectives: Artificial neural networks (ANNs) are flexible and nonlinear models which can be used by clinical oncologists in medical research as decision making tools. This study aimed to predict distant metastasis (DM) of colorectal cancer (CRC) patients using an ANN model. Methods: The data of this study were gathered from 1219 registered CRC patients at the Research Center for Gastroenterology and Liver Disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran (January 2002 and October 2007). For prediction of DM in CRC patients, neural network (NN) and logistic regression (LR) models were used. Then, the concordance index (C index) and the area under receiver operating characteristic curve (AUROC) were used for comparison of neural network and logistic regression models. Data analysis was performed with R 2.14.1 software. Results: The C indices of ANN and LR models for colon cancer data were calculated to be 0.812 and 0.779, respectively. Based on testing dataset, the AUROC for ANN and LR models were 0.82 and 0.77, respectively. This means that the accuracy of ANN prediction was better than for LR prediction. Conclusion: The ANN model is a suitable method for predicting DM and in that case is suggested as a good classifier that usefulness to treatment goals.

A prediction of overall survival status by deep belief network using Python® package in breast cancer: a nationwide study from the Korean Breast Cancer Society

  • Ryu, Dong-Won
    • Korea Journal of Artificial Intelligence
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    • v.6 no.2
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    • pp.11-15
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    • 2018
  • Breast cancer is one of the leading causes of cancer related death among women. So prediction of overall survival status is important into decided in adjuvant treatment. Deep belief network is a kind of artificial intelligence (AI). We intended to construct prediction model by deep belief network using associated clinicopathologic factors. 103881 cases were found in the Korean Breast Cancer Registry. After preprocessing of data, a total of 15733 cases were enrolled in this study. The median follow-up period was 82.4 months. In univariate analysis for overall survival (OS), the patients with advanced AJCC stage showed relatively high HR (HR=1.216 95% CI: 0.011-289.331, p=0.001). Based on results of univariate and multivariate analysis, input variables for learning model included 17 variables associated with overall survival rate. output was presented in one of two states: event or cencored. Individual sensitivity of training set and test set for predicting overall survival status were 89.6% and 91.2% respectively. And specificity of that were 49.4% and 48.9% respectively. So the accuracy of our study for predicting overall survival status was 82.78%. Prediction model based on Deep belief network appears to be effective in predicting overall survival status and, in particular, is expected to be applicable to decide on adjuvant treatment after surgical treatment.

Preventive and Risk Reduction Strategies for Women at High Risk of Developing Breast Cancer: a Review

  • Krishnamurthy, Arvind;Soundara, Viveka;Ramshankar, Vijayalakshmi
    • Asian Pacific Journal of Cancer Prevention
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    • v.17 no.3
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    • pp.895-904
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    • 2016
  • Breast cancer is the most commonly diagnosed invasive cancer among women. Many factors, both genetic and non-genetic, determine a woman's risk of developing breast cancer and several breast cancer risk prediction models have been proposed. It is vitally important to risk stratify patients as there are now effective preventive strategies available. All women need to be counseled regarding healthy lifestyle recommendations to decrease breast cancer risk. As such, management of these women requires healthcare professionals to be familiar with additional risk factors so that timely recommendations can be made on surveillance/risk-reducing strategies. Breast cancer risk reduction strategies can be better understood by encouraging the women at risk to participate in clinical trials to test new strategies for decreasing the risk. This article reviews the advances in the identification of women at high risk of developing breast cancer and also reviews the strategies available for breast cancer prevention.

Prediction and Analysis of Ligands against Estrogen Related Receptor Alpha

  • Chitrala, Kumaraswamy Naidu;Yeguvapalli, Suneetha
    • Asian Pacific Journal of Cancer Prevention
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    • v.14 no.4
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    • pp.2371-2375
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    • 2013
  • Breast cancer is one of the most common malignancies in women around the world. Among the various hormonal types of breast cancer, those that are estrogen receptor (ER) positive account for the majority. Among the estrogen related receptors, estrogen related receptor ${\alpha}$ is known to have a potential role in breast cancer and is one of the therapeutic target. Hence, prediction of novel ligands interact with estrogen related receptor alpha is therapeutically important. The present study, aims at prediction and analysis of ligands from the KEGG COMPOUND database (containing 10,739 entries) able to interact against estrogen receptor alpha using a similarity search and molecular docking approach.

Statistical Analysis for Feature Subset Selection Procedures.

  • Kim, In-Young;Lee, Sun-Ho;Kim, Sang-Cheol;Rha, Sun-Young;Chung, Hyun-Cheol;Kim, Byung-Soo
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • pp.101-106
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    • 2003
  • In this paper, we propose using Hotelling's T2 statistic for the detection of a set of a set of differentially expressed (DE) genes in colorectal cancer based on its gene expression level in tumor tissues compared with those in normal tissues and to evaluate its predictivity which let us rank genes for the development of biomarkers for population screening of colorectal cancer. We compared the prediction rate based on the DE genes selected by Hotelling's T2 statistic and univariate t statistic using various prediction methods, a regulized discrimination analysis and a support vector machine. The result shows that the prediction rate based on T2 is better than that of univatiate t. This implies that it may not be sufficient to look at each gene in a separate universe and that evaluating combinations of genes reveals interesting information that will not be discovered otherwise.

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