Design of an ANFIS based Decision Support System for Diabetes Diagnosis

L, Priyadarshini; Shrinivasan, Lakshmi

doi:10.1109/iccsp48568.2020.9182163

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…For instance, the authors in [15][16][17][18] used the ANFIS system to diagnose COVID-19, and the ANFIS model had good accuracy for predicting and diagnosing COVID-19. In [19], the authors proposed an ANFIS model for the prediction of diabetes, and the proposed model attained good results in classification. In [20], the authors used the ANFIS model for breast cancer diagnosis, the suggested approach obtained 86.2% classification accuracy for the breast cancer diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Rashed,

Popescu

2024

Mathematics

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Predicting diseases in the early stages is extremely important. By taking advantage of advances in deep learning and fuzzy logic techniques, a new model is proposed in this paper for disease evaluation depending on the adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) for classification, and the pre-trained DenseNet-201 model for feature extraction, in addition to the whale optimization algorithm (WOA) for feature selection. Two medical databases (chest X-ray and MRI brain tumor) for the diagnosis of two disease types were used as input in the suggested model. The optimization of ANFIS parameters was performed by GA to achieve the optimum prediction capability. DenseNet-201 for feature extraction was employed to obtain better classification accuracy. Having more features sometimes leads to lower accuracy, and this issue can be rectified using a feature selection strategy WOA which gave good results. The proposed model was evaluated utilizing statistical metrics root mean square error (RMSE), mean square error (MSE), standard deviation (STD), and coefficient of determination (R2), and it was compared with the conventional ANFIS model, with the proposed model (ANFIS-GA) showing a superior prediction capability over the ANFIS model. As a result, it can be concluded that the proposed ANFIS-GA model is efficient and has the potential for a robust diseases evaluation with good accuracy. Also, we conclude from this work that integrating optimization algorithms with ANFIS boosts its performance, resulting in a more accurate and reliable model.

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Section: Related Workmentioning

confidence: 99%

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Rashed,

Popescu

2024

Mathematics

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“…Method Accuracy (%) [12] 2019 Fuzzy rules & GWO 81 [13] 2018 Fuzzy rules & GA 87 [14] 2020 ANFIS 86 [15] 2018 ANFIS + GA 96 [18] 2017 ANFIS 85 [16] 2014 ANFIS + KNN 80 [5] 2021 ANN 88 [17] 2020 ANN 86 [19] 2019 DL 86 [20] 2018 NB + DT + SVM 76…”

Section: References Yearmentioning

confidence: 99%

“…Regarding the works in which researchers used ANFIS technology, an ANFIS classification was used by Priyadarshini et al [14]. They used 240 rules generated in MATLAB.…”

Section: Introductionmentioning

confidence: 99%

A proposed approach for diabetes diagnosis using neuro-fuzzy technique

et al. 2022

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Diabetes is a chronic disease characterized by a decrease in pancreatic insulin production. The immune system will be harmed due to this condition, which will raise blood sugar levels. However, early detection of diabetes enables patients to begin treatment on time, therefore reducing or eliminating the risk of severe consequences. One of the most significant challenges in the healthcare unit is disease diagnosis. Traditional techniques of disease diagnosis are manual and prone to inaccuracy. This paper proposed an approach for diagnosing diabetes using the adaptive neuro-fuzzy inference system (ANFIS) based on Pima Indians diabetes dataset (PIDD). The three stages of the proposed approach are pre-processing classification and evaluation. Normalization, imputation, and anomaly detection are part of the pre-processing stage. The pre-processing was done by normalizing the data, replacing the missing values, and using the local outlier factor (LOF) technique. In the classification stage, ANFIS classifiers were trained using the hybrid learning algorithm of the neural network. Finally, the evaluation procedures use the last stage’s sensitivity, specificity, and accuracy metrics. The obtained classification accuracy was 92.77%, and it seemed rather promising compared to the other classification applications for this topic found in the literature.

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“…This method takes diabetes into account, however the patient must visit the doctor at the clinic to receive treatment. In [18,19] diabetic diagnosis was performed using adaptive neuro-fuzzy inference system, by integrating the interpolation of fuzzy logic control and the adaptability through a neural network. The proposed system was not based on the IoT platform and focused only on the accuracy of the diabetic diagnosis procedure.…”

Section: Related Workmentioning

confidence: 99%

IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation

et al. 2022

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High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. However, insulin consumption is affected by several variables, including age, calorific intake, and body weight. The patient must see the doctor on a regular basis in order to determine the appropriate dose. Nonetheless, hospital facilities are finding it increasingly difficult to treat patients as the number of patients rises; thus, the healthcare industry is searching for an efficient method that can alleviate their burden by assisting patients with chronic conditions through remote patient care. In this work, we have developed an expert system to provide remote treatment for diabetic patients. Our expert system consists of two distinct components: one for the patient and one for the hospital. The sole requirement for the patient will be a wearable device that captures and transmits all relevant data to the cloud. On the hospital side, there should be a system in place to process that data in the cloud. The system employs a fuzzy system to handle data in two stages. A fuzzy system is initially employed to identify whether or not a patient is diabetic. In the second stage, a fuzzy system is utilized to determine the insulin dosage for a diabetic patient. Using sensors and the ESP8266 platform, we have developed a prototype of patient-side hardware. The MATLAB fuzzy toolbox is used for the processing part, which includes fuzzy systems, and the results of the MATLAB analysis are presented in the form of simulation results to demonstrate the accuracy of the proposed system in terms of determining insulin dosage. The results of the simulation using the fuzzy toolbox for the insulin dose of the diabetic patient are significantly close to the amount of dosage prescribed by the endocrinologist.

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Design of an ANFIS based Decision Support System for Diabetes Diagnosis

Cited by 13 publications

References 15 publications

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

Medical Image-Based Diagnosis Using a Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with a Deep Network Model for Features Extraction

A proposed approach for diabetes diagnosis using neuro-fuzzy technique

IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation

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