Artificial Neural Network for Diagnosis of Pancreatic Cancer

M.U, Sanoob; Madhu, A; K.R, Ajesh; Varghese, Surekha Mariam

doi:10.5121/ijci.2016.5205

Cited by 12 publications

(9 citation statements)

References 6 publications

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“…The artificial neural network (ANN), which is based on the brain's neural structure (Rosenblatt, 1958), raised the interest of scientific community worldwide in the field of medicine due to its potential for diagnostic and prognostic applications (Smith et al, 1988;Salim, 2004;Kamruzzaman et al, 2010;Patil and Mudholkar, 2012). It has been used in heart disease (Kamruzzaman et al, 2010), predicting headache, pre-diagnosis of hypertension (Sumathi and Santhakumaran, 2011), kidney stone diseases (Kumar and Abhishek, 2012), classifying breast masses to identify breast cancer (Das and Bhattacharya, 2008;Pandey et al, 2012), dermatologist-level classification of skin diseases/cancer (Bakpo and Kabari, 2011;Esteva et al, 2017), prediction of skin cancer and blood cancer (Payandeh et al, 2009;Esteva et al, 2017;Roffman et al, 2018a), and diagnosis of PC (Sanoob et al, 2016). As an example of the workflow in these applications, classification of skin cancer was performed via a single convolutional neural network, which was trained with a dataset of 129,450 clinical images (Esteva et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…As an example of the workflow in these applications, classification of skin cancer was performed via a single convolutional neural network, which was trained with a dataset of 129,450 clinical images (Esteva et al, 2017). In another study, an ANN model was created to diagnose PC based on a data set of symptoms (Sanoob et al, 2016). A total sample of 120 patients (i.e., 90 training samples and 30 testing samples) with 11 possible symptoms and 3 outcomes were considered for this model (Sanoob et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In another study, an ANN model was created to diagnose PC based on a data set of symptoms (Sanoob et al, 2016). A total sample of 120 patients (i.e., 90 training samples and 30 testing samples) with 11 possible symptoms and 3 outcomes were considered for this model (Sanoob et al, 2016). The authors claimed that the ANN model has advantages over typical strategies for disease diagnosis (Sanoob et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…A total sample of 120 patients (i.e., 90 training samples and 30 testing samples) with 11 possible symptoms and 3 outcomes were considered for this model (Sanoob et al, 2016). The authors claimed that the ANN model has advantages over typical strategies for disease diagnosis (Sanoob et al, 2016). Roffman et al (2018a) took a novel approach to predict nonmelanoma skin cancer by using personal health data (e.g., gender, race, Hispanic ethnicity, hypertension, heart disease, exercise habits, history of stroke, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Pancreatic Cancer Prediction Through an Artificial Neural Network

Muhammad

Hart

Nartowt

et al. 2019

Front. Artif. Intell.

116

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Early detection of pancreatic cancer is challenging because cancer-specific symptoms occur only at an advanced stage, and a reliable screening tool to identify high-risk patients is lacking. To address this challenge, an artificial neural network (ANN) was developed, trained, and tested using the health data of 800,114 respondents captured in the National Health Interview Survey (NHIS) and Pancreatic, Lung, Colorectal, and Ovarian cancer (PLCO) datasets, together containing 898 patients diagnosed with pancreatic cancer. Prediction of pancreatic cancer risk was assessed at an individual level by incorporating 18 features into the neural network. The established ANN model achieved a sensitivity of 87.3 and 80.7%, a specificity of 80.8 and 80.7%, and an area under the receiver operating characteristic curve of 0.86 and 0.85 for the training and testing cohorts, respectively. These results indicate that our ANN can be used to predict pancreatic cancer risk with high discriminatory power and may provide a novel approach to identify patients at higher risk for pancreatic cancer who may benefit from more tailored screening and intervention.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pancreatic Cancer Prediction Through an Artificial Neural Network

Muhammad

Hart

Nartowt

et al. 2019

Front. Artif. Intell.

116

View full text Add to dashboard Cite

show abstract

“…However, this model only determined the accuracy and number of features, without considering other methods and the empirical risk function. [23] developed a neural network CPM to diagnose Pancreatic Cancer disease. Patient's previous medical records containing the symptoms as well as the Doctor's opinion were used in training the ANN to detect the presence or absence of pancreatic cancer in that patient.…”

Section: Related Workmentioning

confidence: 99%

A Logical Approach for Empirical Risk Minimization in Machine Learning for Data Stratification

Taiwo¹,

Awodele²,

Kuyoro³

2017

RJMCS

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The data-driven methods capable of understanding, mimicking and aiding the information processing tasks of Machine Learning (ML) have been applied in an increasing range over the past years in diverse areas at a very high rate, and had achieved great success in predicting and stratifying given data instances of a problem domain. There has been generalization on the performance of the classifier to be the optimal based on the existing performance benchmarks such as accuracy, speed, time to learn, number of features, comprehensibility, robustness, scalability and interpretability. However, these benchmarks alone do not guarantee the successful adoption of an algorithm for prediction and stratification since there may be an incurring risk in its adoption. Therefore, this paper aims at developing a logical approach for using Empirical Risk Minimization (ERM) technique to determine the machine learning classifier with the minimum risk function for data stratification. The generalization on the performance of optimal algorithm was tested on BayesNet, Multilayered perceptron, Projective Adaptive Resonance Theory (PART) and Logistic Model Trees algorithms based on existing performance benchmarks such as correctly classified instances, time to build, kappa statistics, sensitivity and specificity to determine the algorithms with great performances. The study showed that PART and Logistic Model Trees algorithms perform well than others. Hence, a logical approach to apply Empirical Risk Minimization technique on PART and Logistic Model Trees algorithms is shown to give a detailed procedure of determining their empirical risk function to aid the decision of choosing an algorithm to be the best fit classifier for data stratification. This therefore serves as a benchmark for selecting an optimal algorithm for stratification and prediction alongside other benchmarks.

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