A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

Polat, Kemal; Şahan, Seral; Güneş, Salih

doi:10.1016/j.eswa.2006.02.007

Cited by 77 publications

(47 citation statements)

References 6 publications

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“…AIS models were successfully applied to biological and medical problems, such as classification of gene expression data [96,97], identification of breast cancer [98,99], diagnosis of lung cancer [100,101], classification of liver disorders [98,102], detection of heart diseases [103,104], recognition of ECG arrhythmia [105], diagnosis of thyroid diseases [106], and interpretation of carotid artery Doppler signals [107]. The protein structure prediction was investigated with AIS for models based on Dill's lattice approach [108,109] and with three-dimensional models [110].…”

Section: Artificial Immune Systemsmentioning

confidence: 99%

Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

Ivanciuc

2008

CTMC

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Abstract:The drug discovery and development process is lengthy and expensive, and bringing a drug to market may take up to 18 years and may cost up to 2 billion $US. The extensive use of computer-assisted drug design techniques may considerably increase the chances of finding valuable drug candidates, thus decreasing the drug discovery time and costs. The most important computational approach is represented by structure-activity relationships that can discriminate between sets of chemicals that are active/inactive towards a certain biological receptor. An adverse effect of some cationic amphiphilic drugs is phospholipidosis that manifests as an intracellular accumulation of phospholipids and formation of concentric lamellar bodies. Here we present structure-activity relationships (SAR) computed with a wide variety of machine learning algorithms trained to identify drugs that have phospholipidosis inducing potential. All SAR models are developed with the machine learning software Weka, and include both classical algorithms, such as k-nearest neighbors and decision trees, as well as recently introduced methods, such as support vector machines and artificial immune systems. The best predictions are obtained with support vector machines, followed by a perceptron artificial neural network, logistic regression, and k-nearest neighbors.

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Section: Artificial Immune Systemsmentioning

confidence: 99%

Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

Ivanciuc

2008

CTMC

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“…Thyroid function diagnosis is an important classification issue. Proper interpretation of the thyroid data, besides clinical examination and complementary investigation, is an important problem in the diagnosis of thyroid disease [1,2,4]. …”

Section: Introductionmentioning

confidence: 99%

An Experimental Study on Hypothyroid Using Rotation Forest

Gaikwad¹,

Pise²

2014

IJDKP

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“…The overall error rate equals the average of the error rates for each subset. The average of these results provides the test accuracy of the proposed algorithm (Dogantekin, Dogantekin, & Avci, 2010;Polat et al, 2007).…”

Section: Experimental Workmentioning

confidence: 99%

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Pérez‐Rodríguez

Marcano-Cedeño

Costa

et al. 2012

Expert Systems with Applications

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Objective: This research is focused in the creation and validation of a solution to the inverse kinematics problem for a 6 degrees of freedom human upper limb. This system is intended to work within a realtime dysfunctional motion prediction system that allows anticipatory actuation in physical Neurorehabilitation under the assisted-as-needed paradigm. For this purpose, a multilayer perceptron-based and an ANFIS-based solution to the inverse kinematics problem are evaluated. Materials and methods: Both the multilayer perceptron-based and the ANFIS-based inverse kinematics methods have been trained with three-dimensional Cartesian positions corresponding to the end-effector of healthy human upper limbs that execute two different activities of the daily life: 'serving water from a jar' and 'picking up a bottle'. Validation of the proposed methodologies has been performed by a 10 fold cross-validation procedure. Results: Once trained, the systems are able to map 3D positions of the end-effector to the corresponding healthy biomechanical configurations. A high mean correlation coefficient and a low root mean squared error have been found for both the multilayer perceptron and ANFIS-based methods. Conclusions:The obtained results indicate that both systems effectively solve the inverse kinematics problem, but, due to its low computational load, crucial in real-time applications, along with its high performance, a multilayer perceptron-based solution, consisting in 3 input neurons, 1 hidden layer with 3 neurons and 6 output neurons has been considered the most appropriated for the target application.

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A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted pre-processing for thyroid disease diagnosis

Cited by 77 publications

References 6 publications

Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

An Experimental Study on Hypothyroid Using Rotation Forest

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

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