Prognosis of prostate cancer by artificial neural networks

Sarıtaş, İsmail; Özkan, İlker Ali; Sert, İbrahim Ünal

doi:10.1016/j.eswa.2010.03.056

Cited by 53 publications

(25 citation statements)

References 12 publications

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“…Daily image guidance device, reposition and evaluation by the physician are indisputable advantages in the quality of treatment [36,37]. Patients' cohort and daily image analysis can highlight inappropriate setup, criticalities or anatomical variations, which can contribute to rate toxicity and to treatment course objectives.…”

Section: Discussionmentioning

confidence: 99%

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

et al. 2015

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Section: Discussionmentioning

confidence: 99%

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

et al. 2015

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“…In this study, the number of required layers in the network was selected through testing, repeating and comparing the results; the obtained results indicate more precision of the system using double layer networks. The first layer, also called “hidden layer” has some neurons or neural units because in previous studies, the number of hidden layer neurons was very effective in increasing network function (26, 28-35). In the present study, hidden layer neurons of neural network were considered at 1-50 in order to increase the performance of the survival prediction model; network performance with the current topology was calculated with a different number of neurons in the hidden layer and the structure with an 8-neuron topology in one hidden layer was selected.…”

Section: Methodsmentioning

confidence: 99%

Comparing Three Data Mining Methods to Predict Kidney Transplant Survival

Shahmoradi¹,

Langarizadeh²,

Pourmand³

et al. 2016

Acta Inform Med

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Introduction:One of the most important complications of post-transplant is rejection. Analyzing survival is one of the areas of medical prognosis and data mining, as an effective approach, has the capacity of analyzing and estimating outcomes in advance through discovering appropriate models among data. The present study aims at comparing the effectiveness of C5.0 algorithms, neural network and C&RTree to predict kidney transplant survival before transplant.Method:To detect factors effective in predicting transplant survival, information needs analysis was performed via a researcher-made questionnaire. A checklist was prepared and data of 513 kidney disease patient files were extracted from Sina Urology Research Center. Following CRISP methodology for data mining, IBM SPSS Modeler 14.2, C5.0, C&RTree algorithms and neural network were used.Results:Body Mass Index (BMI), cause of renal dysfunction and duration of dialysis were evaluated in all three models as the most effective factors in transplant survival. C5.0 algorithm with the highest validity (96.77%) was the first in estimating kidney transplant survival in patients followed by C&RTree (83.7%) and neural network (79.5%) models.Conclusion:Among the three models, C5.0 algorithm was the top model with high validity that confirms its strength in predicting survival. The most effective kidney transplant survival factors were detected in this study; therefore, duration of transplant survival (year) can be determined considering the regulations set for a new sample with specific characteristics.

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“…It's concluded that ANN will help the doctors make a quicker and reliable diagnosis which allows them to offer the best treatment. [11] Most of the research papers highlighted above show good results with the use of ANN and Decision trees. ANN are considered to have more complex structures and this complexity gives this algorithm the power to classify any data with complex relationships.…”

Section: Application Of Data Mining In Cancermentioning

confidence: 98%

Use of data mining to check the prevalence of prostate cancer: Case of Nairobi County

Ngaruiya

Moturi

2015

2015 IST-Africa Conference

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Prostate cancer has been on the rise in the past years and alarming cases being found in men in their 20's. The problem is that most of the cases are diagnosed in their late stages thus the mortality rate being high. In recent years data driven analytic studies have become a common complement with novel research where different tools and algorithms are taking a centre stage in cancer research. In this research, the main objective was to use data mining to derive patterns which were used in building a prognostic tool that helps in identification of the Gleason score once screened and deciding the treatment technique. In this research, we used two popular data mining tools (R Environment and WEKA) which exhibited almost same results .The dataset contained around 485 records and 7 variables. In WEKA, a 10-fold cross-validation was used in model building and comparison between ANN and J48. The results showed that ANN is the most accurate predictor compared to J48 in all the instances displaying varying levels in the different zones created. This study contributes to society, academics and cancer research which ultimately assist in reduction of mortality rates by use of pattern recognitions which leads to better decision making. Furthermore, this is a potential impact in helping the GOK (Government of Kenya) in establishing where they should correctly place the cancer diagnosis and treatment equipment that were rolled out by the National government early 2015.

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Prognosis of prostate cancer by artificial neural networks

Cited by 53 publications

References 12 publications

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

Comparing Three Data Mining Methods to Predict Kidney Transplant Survival

Use of data mining to check the prevalence of prostate cancer: Case of Nairobi County

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