Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya, _; Odedra, Devang; Samanta, Subir; Vidyarthi, Ambarish Sharan

doi:10.1900/rds.2010.7.252

Cited by 72 publications

(31 citation statements)

References 36 publications

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“…11 The tool designed in this study is distinguished from other tools by several features. It uses only eight inputs to make prediction.…”

Section: Discussionmentioning

confidence: 99%

“…A comprehensive review in this regard presents interesting details of several such studies and current trends. 11 Neural nets try to simulate the human brain's ability to learn. That is, the artificial neural net is also made of neurons and dendrites.…”

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confidence: 99%

“…Once a neural net had been trained correctly, it will probably be able to find the desired output to a given input that had been learned, by using these matrix values. 11 In the present study, the mixture of experts (ME) model 12 of a neural network was trained with the expectation-minimization algorithm and successfully incorporated into a computer interface where medical practitioners can enter the patient data and get the diagnosis.…”

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confidence: 99%

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Java-Based Diabetes Type 2 Prediction Tool for Better Diagnosis

Shankaracharya

Odedra

Mallick

et al. 2012

Diabetes Technology & Therapeutics

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Background: The concept of classification of clinical data can be utilized in the development of an effective diagnosis system by taking the advantage of computational intelligence. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important problem in neural networks. Unfortunately, although several classification studies have been carried out with significant performance, many of the current methods often fail to reach out to patients. Graphical user interface-enabled tools need to be developed through which medical practitioners can simply enter the health profiles of their patients and receive an instant diabetes prediction with an acceptable degree of confidence. Methods: In this study, the neural network approach was used for a dataset of 768 persons from a Pima Indian population living near Phoenix, AZ. A neural network mixture of experts model was trained with these data using the expectation-minimization algorithm. Results: The mixture of experts method was used to train the algorithm with 97% accuracy. A graphical user interface was developed that would work in conjunction with the trained network to provide the output in a presentable format. Conclusions: This study provides a machine-implementable approach that can be used by physicians and patients to minimize the extent of error in diagnosis. The authors are hopeful that replication of results of this study in other populations may lead to improved diagnosis. Physicians can simply enter the health profile of patients and get the diagnosis for diabetes type 2.

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“…11 The tool designed in this study is distinguished from other tools by several features. It uses only eight inputs to make prediction.…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Java-Based Diabetes Type 2 Prediction Tool for Better Diagnosis

Shankaracharya

Odedra

Mallick

et al. 2012

Diabetes Technology & Therapeutics

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“…The early contributions can be found on the neural networks, it provides a new significant way for intelligent medical diagnosis. Based on this idea, artificial neural networks have been applied in the diagnosis of: (i) pancreatic disease [1], (ii) gynecological diseases [2], (iii) early diabetes [3], (iv) colorectal cancer [4], and (v) multiple sclerosis lesions [5]. While this kind of method to set up the achievements of medical diagnostic system is still limited, the main reasons are that the learning algorithm cannot calculate the right results when the required algorithm to set up neural network model solves the larger, multi-features disease diagnosis problems.…”

Section: Introductionmentioning

confidence: 99%

Agent-based intelligent medical diagnosis system for patients

Zhang¹,

Liu²,

Zhu³

et al. 2015

THC

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Abstract. BACKGROUND:According to the analysis of the challenges faced by the current public health circumstances such as the sharp increase in elderly patients, limited medical personnel, resources and technology, the agent-based intelligent medical diagnosis system for patients (AIMDS) is proposed in this research. OBJECTIVE: Based on advanced sensing technology and professional medical knowledge, the AIMDS can output the appropriate medical prescriptions and food prohibition when the physical signs and symptoms of the patient are inputted. METHODS: Three core modules are designed include sensing module, intuition-based fuzzy set theory/medical diagnosis module, and medical knowledge module. RESULTS: The result shows that the optimized prescription can reach the desired level, with great curative effect for patient disease, through a case study simulation. CONCLUSION: The presented AIMDS can integrate sensor technique and intelligent medical diagnosis methods to make an accurate diagnosis, resulting in three-type of optimized descriptions for patient selection.

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“…In this field, the neural network methods have proven their efficiency as a diagnosing tool. Indeed, since the study performed by Szolovits et al [32] many studies have been published such as colorectal cancer [33], multiple sclerosis lesions [34], colon cancer [35], pancreatic disease [36], gynecological diseases [37], and early diabetes [38]. Readers may refer to Amato et al [39] for more details.…”

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confidence: 99%