A decision support system using classification of the blood glucose and HbA1C level classes from palm perspiration data
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Summary
Artificial neural network structures were used for the classification of the relationship between blood data values and palm perspiration rate as a non-invasive measurement technique and a comparative study was realized by using feed forward multilayer, Elman, probabilistic, radial basis and learning vector quantisation neuralnetwork structures.
- Type
- article
- Published
- 2012-05-02
- Cited by
- 1
- References
- 25
- OpenAlex
- https://openalex.org/W41212367
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:37042205
Keywords
Perspiration, Artificial neural network, Artificial intelligence, Support vector machine, Data set
References
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- A study on non‐invasive detection of blood glucose concentration from human palm perspiration by using artificial neural networks
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- Probabilistic neural networks
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- Improved versions of learning vector quantization
- A Comparative Study on Chronic Obstructive Pulmonary and Pneumonia Diseases Diagnosis using Neural Networks and Artificial Immune System
- Tuberculosis Disease Diagnosis Using Artificial Neural Networks
- A comparative study on thyroid disease diagnosis using neural networks
- Chest diseases diagnosis using artificial neural networks
- A Study on Chronic Obstructive Pulmonary Disease Diagnosis Using Multilayer Neural Networks
- An electronic nose and modular radial basis function network classifiers for recognizing multiple fragrant materials
- A study on transient and steady state sensor data for identification of individual gas concentrations in their gas mixtures
- A comparative study on diabetes disease diagnosis using neural networks
- Determination of Blood Glucose Level-Based Breath Analysis by a Quartz Crystal Microbalance Sensor Array
- Mesomorphic phthalocyanine as chemically sensitive coatings for chemical sensors
- On the learning and convergence of the radial basis networks
- Piezoelectric Sorption Detector.
- MEDICAL DIAGNOSIS ON PIMA INDIAN DIABETES USING GENERAL REGRESSION NEURAL NETWORKS
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