scholarly journals Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Neha Sharma ◽  
Hari Om
Keyword(s):  
Neural Network ◽  
Oral Cancer ◽  
Early Detection ◽  
Clinical Symptoms ◽  
Probabilistic Neural Network ◽  
Cost Effective ◽  
General Regression Neural Network ◽  
Oral Cancers ◽  
General Regression ◽  
Early Detection And Prevention

In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN) for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

Neural Network Performance Comparison in Infant Pain Expression Classifications

2013 ◽  
Vol 475-476 ◽  
pp. 1104-1109
Author(s):  
Muhammad Naufal Mansor ◽  
Mohd Nazri Rejab
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Probabilistic Neural Network ◽  
Performance Comparison ◽  
General Regression Neural Network ◽  
Pain Expression ◽  
Different Types ◽  
Infant Pain ◽  
Value Decomposition ◽  
General Regression

Infant pain is a non-stationary made by infants in response to certain situations. This infant facial expression can be used to identify physical or psychology status of infant. The aim of this work is to compare the performance of features in infant pain classification. Fast Fourier Transform (FFT), and Singular value Decomposition (SVD) features are computed at different classifier. Two different case studies such as normal and pain are performed. Two different types of radial basis artificial neural networks namely, Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) are used to classify the infant pain. The results emphasized that the proposed features and classification algorithms can be used to aid the medical professionals for diagnosing pathological status of infant pain.

scholarly journals An Optimized Recursive General Regression Neural Network Oracle for the Prediction and Diagnosis of Diabetes

2018 ◽  
pp. 1-11 ◽  
Author(s):  
Dana Bani-Hani ◽  
Pruthak Patel ◽  
Tasneem Alshaikh
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Prediction Models ◽  
Probabilistic Neural Network ◽  
General Regression Neural Network ◽  
Medical Decision ◽  
Support Vector ◽  
Pima Indians ◽  
K Nearest Neighbor ◽  
General Regression

Diabetes is a serious, chronic disease that has been seeing a rise in the number of cases and prevalence over the past few decades. It can lead to serious complications and can increase the overall risk of dying prematurely. Data-oriented prediction models have become effective tools that help medical decision-making and diagnoses in which the use of machine learning in medicine has increased substantially. This research introduces the Recursive General Regression Neural Network Oracle (RGRNN Oracle) and is applied on the Pima Indians Diabetes dataset for the prediction and diagnosis of diabetes. The R-GRNN Oracle (Bani-Hani, 2017) is an enhancement to the GRNN Oracle developed by Masters et al. in 1998, in which the recursive model is created of two oracles: one within the other. Several classifiers, along with the R-GRNN Oracle and the GRNN Oracle, are applied to the dataset, they are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). Genetic Algorithm (GA) was used for feature selection as well as the hyperparameter optimization of SVM and MLP, and Grid Search (GS) was used to optimize the hyperparameters of KNN and RF. The performance metrics accuracy, AUC, sensitivity, and specificity were recorded for each classifier.

scholarly journals Early detection and prevention of oral cancer: an appraisal

2019 ◽  
Vol 7 (1) ◽  
pp. 367
Author(s):  
Swati Agrawal ◽  
Vrinda Saxena ◽  
Swapnil Jain ◽  
Vijayta Sharva ◽  
Shibani Sarangi ◽  
...  
Keyword(s):  
Oral Cancer ◽  
Early Detection ◽  
Early Stage ◽  
Grey Literature ◽  
Premalignant Lesions ◽  
World Health ◽  
Cochrane Library ◽  
Global Youth Tobacco Survey ◽  
Oral Cancers ◽  
Early Detection And Prevention

Oral cancers (OC) represent the majority of head and neck cancers with more than half million patients being affected each year worldwide. Oral squamous cell carcinoma is the sixth commonest cancer worldwide, accounting for approximately 4% of all cancers. Therefore, an improvement in the prevention and control of oral cancer is of critical importance. This may be achieved by reducing the risk through avoidance of tobacco and alcohol, recognizing and treating premalignant lesions and detecting developed OC at an early stage. Data search for the present review was done electronically. Electronic search was conducted using databases such as Pubmed and Medline, Cochrane library, articles published in peer-reviewed journals, text books, grey literature and from sites of World Health Organization, Centre For Diseases Control Report, Global Adult Tobacco Survey and Global Youth Tobacco Survey reports. This review ruled out web of causation and web of prevention including early detection and prevention to be an ideal strategy to reduce the prevalence of oral cancer and its impact on quality of life. Screening and early detection in population at risk have been proposed to decrease both morbidity and mortality associated with the oral cancer.

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

scholarly journals A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mina Salehi ◽  
Siamak Farhadi ◽  
Ahmad Moieni ◽  
Naser Safaie ◽  
Mohsen Hesami
Keyword(s):  
Neural Network ◽  
Fruit Fly ◽  
Corylus Avellana ◽  
General Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Harvesting Time ◽  
Fruit Fly Optimization ◽  
General Regression ◽  
Concentration Levels

Abstract Background Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. Results GRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1). Conclusions Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

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