The Early Detection of Diabetes Mellitus (DM) Using Fuzzy Hierarchical Model

Lukmanto, Rian Budi; Irwansyah, Edy

doi:10.1016/j.procs.2015.07.571

Cited by 33 publications

(13 citation statements)

References 17 publications

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“…To decide whether a person has the possibility of diabetic or not, the author used the fuzzy determination system to asses rules with the fuzzy operator in their study and portray knowledge with descriptions. Lukmanto [37] proposed an intelligence system by using a fuzzy hierarchical model that can perform initial diagnosis against diabetes. The proposed model was implemented on 311 relevant data and acquired an accuracy of 87.46 % as equivalent to a medical doctor's statement.…”

Section: A Existing Work Using Fuzzy Methodsmentioning

confidence: 99%

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

et al. 2020

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Section: A Existing Work Using Fuzzy Methodsmentioning

confidence: 99%

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

et al. 2020

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“…Whilst promising, interesting work remains. This includes: examining the influence of crisp rule bases of a different complexity upon the performance of the final fuzzy rules and the effect of any subsequent fine-tuning with ANFIS; investigating the use of more powerful data discretisation techniques (e.g., [18]); and comparing the proposed approach with alternative methods (e.g., [19]) that employ fuzzy systems for diabetes detection.…”

Section: Resultsmentioning

confidence: 99%

Effective Diagnosis of Diabetes with a Decision Tree-Initialised Neuro-fuzzy Approach

Chen

Shang²,

et al. 2018

Advances in Intelligent Systems and Computing

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Diabetes mellitus is a serious hazard to human health that can result in a number of severe complications. Early diagnosis and treatment is of significant importance to patients for the acquisition of a better quality life and precaution against subsequent complications. This paper proposes an approach by learning a fuzzy rule base for the effective diagnosis of diabetes mellitus. In particular, the proposed approach starts with the generation of a crisp rule base through a decision tree learning mechanism, which is data-driven and able to learn simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the powerful neuro-fuzzy framework of ANFIS, further optimising the parameters of both rule antecedents and consequents. Experimental study on the well-known Pima Indian diabetes data set is provided to demonstrate the promising potential of the proposed approach.

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“…The IoT devices and the sensor reading stored in the cloud can be used to diagnose the disease at the appropriate time, prior to severity of the disease. A comprehensive diagnosis process, disease prevention, manual and psychological impairments in humans come under the healthcare monitoring [22]. The field of healthcare evolves rapidly in most of the countries.…”

Section: Related Studiesmentioning

confidence: 99%

Hybrid Multi Cloud based Disease Prediction Model for Type II Diabetes

Durgadevi,

Kalpana

2020

IJEAT

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Advancements in health informatics pave the way to explore new medical decision making systems which are characterized by an exponential evolution of knowledge. In the medical domain, disease prediction has become the centre of research with the increasing trend of healthcare applications. The predictive knowledge for the diagnosis of disease highly depends on the subjective knowledge of the experts. So the development of a disease prediction model in time is essential for patients and physicians to overcome the problem of medical distress. This paper explores a hybrid approach (Cooperative Ant Miner Genetic Algorithm) for classifying the medical data. Three benchmarked Type II diabetic datasets (US, PIMA, German) from the UCI machine learning repository were used to analyze the effectiveness of the disease prediction model. The devised classification algorithm with a Soft-Set approach was deployed in a Multi-Cloud environment for enhancing the storage and retrieval of data with reduced response and computation time. The cooperative classification algorithm in the cloud database distinguishes the diseased cases from the normal ones .The soft set theory analyzes the severity of the diseased cases by calculating the percentage of diabetic risk using soft intelligent rules and stores them in a separate knowledge base. Thus the proposed model serves as a suitable tool for eliciting and representing the expert’s decision which aids in prediction of Type II diabetic risk percentage leading to the timely treatment of patients.

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The Early Detection of Diabetes Mellitus (DM) Using Fuzzy Hierarchical Model

Cited by 33 publications

References 17 publications

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Effective Diagnosis of Diabetes with a Decision Tree-Initialised Neuro-fuzzy Approach

Hybrid Multi Cloud based Disease Prediction Model for Type II Diabetes

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