Breast Cancer Prediction Using Soft Computing Techniques A Survey

Durairaj, M.; Priya, K. Padma

doi:10.26438/ijcse/v6i8.135145

Cited by 6 publications

(11 citation statements)

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“…In this work, the Pima Indian Diabetes dataset was used for comparing various data mining algorithms with prediction accuracy. The table [26] is illustrated below. Table 1 Performance of Different Data Mining Algorithms [26] From the above literature survey, it is observed that the ANN provides more accurate result than other classification techniques such as SVM, KNN and C4.5.…”

Section: Resultsmentioning

confidence: 99%

“…The artificial neural network is as similar as natural neural network of a human brain. Artificial Neural Network mainly has three layers, they are; Input layer: Input neurons define all the input attribute values for the data mining model and their probabilities [26]. Hidden layer: Hidden neurons receive inputs from input neurons and provide outputs to output neurons.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The neuron with greater weight is assigned to an input. The value of that input is more important weights can be negative, which means that the input can inhibit rather than favour a specific result [26]. editor@iaeme.com Output layer: Output neurons represent predictable attribute values for the data mining model [26].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Systems that construct classifiers are one of the commonly used data mining tools. Such systems take a collection of cases as input, each case belonging to one of a small number of classes that is described by its values for a fixed set of attributes, and output a classifier that can accurately predict the class to which a new case belongs [26].…”

Section: C45 Algorithmmentioning

confidence: 99%

“…There are several popular techniques used for such evaluation. The reviews of these data mining and Artificial Intelligence techniques lead us to a conclusion that the applications of ANN show very encouraging results comparing with SVM, KNN, and C4.5 similar techniques [26]. editor@iaeme.com Since, ANN is known to be a Universal Predictor, it predicts with relatively accuracy and efficiency.…”

Section: Analysis Of Reviewed Workmentioning

confidence: 99%

See 4 more Smart Citations

Forestalling Growth Rate in Type Ii Diabetic Patients Using Data Mining and Artificial Neural Networks: An Intense Survey

Dubey¹,

Shrivastava²

2019

IJCET

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The race for urbanization and thirst for high living status leads to unhealthy life. As the result a rapid growth in number of diabetic patients in urban areas approaching to its deadline. In this situation it become a prime necessity for physicians and health workers to recognize accurate growth rate in number of diabetic patients. Artificial Neural Network is used as one of the artificial intelligent technique for forestalling growth rate of type II diabetic patients. Diabetes occurred due to increased level of glucose in blood. In this paper, an intense survey is done for the prediction of Type II diabetes using different Data Mining tools and Artificial Neural Network techniques, is presented. This survey is aimed to recognize and propose an effective technique for earlier prediction of the Type II diabetes. The data mining techniques like C4.5 Classifier, Support Vector Machine and K-Nearest Neighbour are compared for this work with Artificial Neural Network. As the results Artificial Neural Network found with a great accuracy of 89%.

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Section: Resultsmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: C45 Algorithmmentioning

confidence: 99%

Section: Analysis Of Reviewed Workmentioning

confidence: 99%

See 3 more Smart Citations

Forestalling Growth Rate in Type Ii Diabetic Patients Using Data Mining and Artificial Neural Networks: An Intense Survey

Dubey¹,

Shrivastava²

2019

IJCET

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A novel method for structure selection of the Recurrent Random Neural Network using multiobjective optimisation

Nepomuceno

2019

Applied Soft Computing

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Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

et al. 2021

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Diabetes is one of the most rapidly growing chronic diseases, which has affected millions of people around the globe. Its diagnosis, prediction, proper cure, and management are crucial. Data mining based forecasting techniques for data analysis of diabetes can help in the early detection and prediction of the disease and the related critical events such as hypo/hyperglycemia. Numerous techniques have been developed in this domain for diabetes detection, prediction, and classification. In this paper, we present a comprehensive review of the state-of-the-art in the area of diabetes diagnosis and prediction using data mining. The aim of this paper is twofold; firstly, we explore and investigate the data mining based diagnosis and prediction solutions in the field of glycemic control for diabetes. Secondly, in the light of this investigation, we provide a comprehensive classification and comparison of the techniques that have been frequently used for diagnosis and prediction of diabetes based on important key metrics. Moreover, we highlight the challenges and future research directions in this area that can be considered in order to develop optimized solutions for diabetes detection and prediction.

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Breast Cancer Prediction Using Soft Computing Techniques A Survey

Cited by 6 publications

References 0 publications

Forestalling Growth Rate in Type Ii Diabetic Patients Using Data Mining and Artificial Neural Networks: An Intense Survey

Forestalling Growth Rate in Type Ii Diabetic Patients Using Data Mining and Artificial Neural Networks: An Intense Survey

A novel method for structure selection of the Recurrent Random Neural Network using multiobjective optimisation

Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

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