Diagnosis of Diabetes Diseases Using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-nearest Neighbor

Chikh, Mohammed Amine; Saidi, Meryem; Settouti, Nesma

doi:10.1007/s10916-011-9748-4

Cited by 99 publications

(31 citation statements)

References 11 publications

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“…a subset of the original features. KNN classifiers are instance-based clasifiers that are used in several medical applications [22,44,66,69]. The KNN algorithm assigns the label of the k training instances that are closer in the feature space using a majority vote.…”

Section: Classificationmentioning

confidence: 99%

Stress Detection Using Wearable Physiological and Sociometric Sensors

Mozos

Sandulescu

Andrews

et al. 2016

Int. J. Neur. Syst.

150

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2 Mozos et al.Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection.

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Section: Classificationmentioning

confidence: 99%

Stress Detection Using Wearable Physiological and Sociometric Sensors

Mozos

Sandulescu

Andrews

et al. 2016

Int. J. Neur. Syst.

150

View full text Add to dashboard Cite

show abstract

“…Applications of artificial intelligence techniques in diagnosing diseases have been increasing, and AIRS is one of these techniques that has been successfully used in medical classification problems (Chikh et al 2012). There are different classification tools that offer different advantages; for instance, k-nearest neighbor is one of the best classifiers for specific problems; however, it is computationally expensive (Goodman et al 2002).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system

et al. 2015

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Breast cancer is the top cancer in women worldwide. Scientists are looking for early detection strategies which remain the cornerstone of breast cancer control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose this disease. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. To increase the classification accuracy, this study introduces a new hybrid system that incorporates support vector machine, fuzzy logic, and real tournament selection mechanism into AIRS. The Wisconsin Breast Cancer data set was used as the benchmark data set; it is available through the machine learning repository of the University of California at Irvine. The classification performance was measured through tenfold cross-validation, student's t test, sensitivity and specificity. With an accuracy of 100 %, the proposed method was able to classify breast cancer dataset successfully.

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“…The k-nearest neighbors are determined based on some distance functions. As it is simplest and oldest approach, there have been so many data mining and pattern recognition applications, such as ventricular arrhythmia detection [3], bankruptcy prediction [4], diagnosis of diabetes diseases [5], human action recognition [6], text categorization [7], and many other successful ones.…”

Section: Introductionmentioning

confidence: 99%

NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier

et al. 2017

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k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parametric supervised classifier. It aims to determine the class label of an unknown sample by its k-nearest neighbors that are stored in a training set. The k-nearest neighbors are determined based on some distance functions. Although k-NN produces successful results, there have been some extensions for improving its precision. The neutrosophic set (NS) defines three memberships namely T, I and F. T, I, and F shows the truth membership degree, the false membership degree, and the indeterminacy membership degree, respectively. In this paper, the NS memberships are adopted to improve the classification performance of the k-NN classifier. A new straightforward k-NN approach is proposed based on NS theory. It calculates the NS memberships based on a supervised neutrosophic c-means (NCM) algorithm. A final belonging membership U is calculated from the NS triples as U = T + I − F. A similar final voting scheme as given in fuzzy k-NN is considered for class label determination. Extensive experiments are conducted to evaluate the proposed method's performance. To this end, several toy and real-world datasets are used. We further compare the proposed method with k-NN, fuzzy k-NN, and two weighted k-NN schemes. The results are encouraging and the improvement is obvious.

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Diagnosis of Diabetes Diseases Using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-nearest Neighbor

Cited by 99 publications

References 11 publications

Stress Detection Using Wearable Physiological and Sociometric Sensors

Stress Detection Using Wearable Physiological and Sociometric Sensors

RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system

NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier

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