Positive Impression of Low-Ranking Microrn as in Human Cancer Classification

Li, Feifei; Piao, Yongjun; Li, Meijing; Piao, Minghao; Ryu, Keun Ho

doi:10.5121/csit.2014.4239

Cited by 1 publication

(1 citation statement)

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“…Ranking the features is a procedure which determines the importance and influence of features on the final prediction result. Due to different data types that can be used to describe a disease in the process of creating predictive model, three techniques of ranking features were used: Information Gain (IG) [14], Gain Ration (GR) [15] Feature Selection (CFS) [16]. All these techniques use entropy as a basic measure for ranking the features.…”

Section: Techniques For Ranking the Featuresmentioning

confidence: 99%

Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model

Trstenjak

Ðonko

Avdagić

2016

Eng. Technol. Appl. Sci. Res.

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Nowadays, we are witnessing the rapid development of medicine and various methods that are used for early detection of diseases. In order to make quality decisions in diagnosis and prevention of disease, various decision support systems based on machine learning methods have been introduced in the medical domain. Such systems play an increasingly important role in medical practice. This paper presents a new web framework concept for disease prediction. The proposed framework is object-oriented and enables online prediction of various diseases. The framework enables online creation of different autonomous prediction models depending on the characteristics of diseases. Prediction process in the framework is based on a hybrid Case Based Reasoning classifier. The framework was evaluated on disease datasets from public repositories. Experimental evaluation shows that the proposed framework achieved high diagnosis accuracy.

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Section: Techniques For Ranking the Featuresmentioning

confidence: 99%