Diabetes Diagnosis using Feature Weighted Support Vector Machines and Modified Cuckoo Search

Giveki, Davar; Ebrahimipour, Seyed Mohammadreza; Soltanshahi, Mohammad Ali; Khademian, Younes

doi:10.5120/9371-9528

Cited by 30 publications

(14 citation statements)

References 14 publications

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“…Knowledge Discovery [14], [15] in Databases is the process of identifying a valid, potentially useful and ultimately understandable structure in data. It involves selecting or sampling data from a data warehouse, cleaning or preprocessing it, transforming or reducing it, applying a data mining component to produce and evaluate a structure.…”

Section: Methodology Used In the Existing System 41 Knowledge Discovmentioning

confidence: 99%

Disease Diagnosis using Soft Computing Model: A Digest

AbdullahAlghamdi¹,

Bhirud²,

Alam³

2014

IJCA

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In today's era, medical diagnosis has become one of the most progressive disciplines. Since people are taking hybrid food in the daily life, different disease came into existence. It made people very careful for their health. In some cases, lack of knowledge of disease in doctor causes the patient death. Therefore, we require such diagnosis system for non-expertise so that right prescription can come for the patient. Over the years, soft computing plays the major role for computer aided disease diagnosis in physician decision process. Considering these issues, a lot work has been devoted to this discipline. Several disease diagnosis systems based on fuzzy, rule based reasoning, case based reasoning etc. have been proposed for different disease using their symptoms. Hepatitis diagnosis system is good example which is based on CBR. In this paper, we reviewed such types of diagnosis system and their techniques used. We also emphasizes on the liver disease diagnosis system. Moreover, we also found shortcoming in the present systems that cause the vague diagnosis system. Hence, patient gets more illness or sometimes costs as death. We also directed future work that will help to make the present system more robust, reliable, and helpful for nonexpertise.

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Section: Methodology Used In the Existing System 41 Knowledge Discovmentioning

confidence: 99%

Disease Diagnosis using Soft Computing Model: A Digest

AbdullahAlghamdi¹,

Bhirud²,

Alam³

2014

IJCA

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“…Cuckoo Search (CS) [2] is another successful metaheuristic which mimics the cuckoo behavior reproduction strategy. Over the last decades, uses of metaheuristic algorithms have increased [10][11][12][13][14][15][16][17]. These algorithms are used to solve complex computational optimization problems, however, fast convergence along with accuracy is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Fractal Search: A powerful metaheuristic algorithm

Salimi

2015

Knowledge-Based Systems

531

237

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“…Nowadays, the applications of metaheuristic in solving problems are increased dramatically [15][16][17][18]. Among the all metaheuristic algorithms, PSO is a population (swarm) based optimization tool.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Investigation the Effect of Particle Swarm Optimization in Performance of Mixture of Experts

Rani¹,

Hatami²,

Meysamiazad³

2014

IJCA

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Mixture of experts (ME) is one of the most popular and interesting combining methods, which has great potential to improve performance in machine learning. ME is established based on the divide-and-conquer principle in which the problem space is divided between a few neural network experts, supervised by a gating network. In earlier works on ME, different strategies were developed to divide the problem space between the experts. As result, we have introduced a new method based on the principles of Particle Swarm Optimization (PSO) as a learning step in ME. In this paper, different aspects of the proposed method are compared with the common version of ME. The result carried out from this paper shows that the new method is robust to the variation of ensemble complexity in terms of the number of individual experts, and the number of hidden units.

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Diabetes Diagnosis using Feature Weighted Support Vector Machines and Modified Cuckoo Search

Cited by 30 publications

References 14 publications

Disease Diagnosis using Soft Computing Model: A Digest

Disease Diagnosis using Soft Computing Model: A Digest

Stochastic Fractal Search: A powerful metaheuristic algorithm

Investigation the Effect of Particle Swarm Optimization in Performance of Mixture of Experts

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