Predicting Diabetes Diagnosis with Binary-To-Fuzzy Extrapolations and Weights Tuned via Genetic Algorithm

2021 ◽  
pp. 321-331
Author(s):  
Katherine L. O’Grady ◽  
Javier Viaña ◽  
Kelly Cohen
Keyword(s):  
Genetic Algorithm ◽  
Diabetes Diagnosis

scholarly journals EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4036 ◽  
Author(s):  
Sushruta Mishra ◽  
Hrudaya Kumar Tripathy ◽  
Pradeep Kumar Mallick ◽  
Akash Kumar Bhoi ◽  
Paolo Barsocchi
Keyword(s):  
Genetic Algorithm ◽  
Type 2 Diabetes ◽  
Multilayer Perceptron ◽  
Optimization Techniques ◽  
Classification Model ◽  
Adaptive Genetic Algorithm ◽  
Diabetes Diagnosis ◽  
Prediction Ability ◽  
Proposed Model

Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times, medical data mining is gaining popularity in complex healthcare problems based disease datasets. Unstructured healthcare data constitutes irrelevant information which can affect the prediction ability of classifiers. Therefore, an effective attribute optimization technique must be used to eliminate the less relevant data and optimize the dataset for enhanced accuracy. Type 2 Diabetes, also called Pima Indian Diabetes, affects millions of people around the world. Optimization techniques can be applied to generate a reliable dataset constituting of symptoms that can be useful for more accurate diagnosis of diabetes. This study presents the implementation of a new hybrid attribute optimization algorithm called Enhanced and Adaptive Genetic Algorithm (EAGA) to get an optimized symptoms dataset. Based on readings of symptoms in the optimized dataset obtained, a possible occurrence of diabetes is forecasted. EAGA model is further used with Multilayer Perceptron (MLP) to determine the presence or absence of type 2 diabetes in patients based on the symptoms detected. The proposed classification approach was named as Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP). It is also implemented on seven different disease datasets to assess its impact and effectiveness. Performance of the proposed model was validated against some vital performance metrics. The results show a maximum accuracy rate of 97.76% and 1.12 s of execution time. Furthermore, the proposed model presents an F-Score value of 86.8% and a precision of 80.2%. The method is compared with many existing studies and it was observed that the classification accuracy of the proposed Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP) model clearly outperformed all other previous classification models. Its performance was also tested with seven other disease datasets. The mean accuracy, precision, recall and f-score obtained was 94.7%, 91%, 89.8% and 90.4%, respectively. Thus, the proposed model can assist medical experts in accurately determining risk factors of type 2 diabetes and thereby help in accurately classifying the presence of type 2 diabetes in patients. Consequently, it can be used to support healthcare experts in the diagnosis of patients affected by diabetes.

Optimal design of type-2 fuzzy systems for diabetes classification based on genetic algorithms

2021 ◽  
pp. 1-18
Author(s):  
Patricia Melin ◽  
Daniela Sánchez
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Life Quality ◽  
Diabetes Diagnosis ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Inference Systems ◽  
Parameter Values

Diabetes has become a global health problem, where a proper diagnosis is vital for the life quality of patients. In this article, a genetic algorithm is put forward for designing type-2 fuzzy inference systems to perform Diabetes Classification. We aim at finding parameter values of Type-2 Trapezoidal membership functions and the type of model (Mamdani or Sugeno) with this optimization. To verify the effectiveness of the proposed approach, the PIMA Indian Diabetes dataset is used, and results are compared with type-1 fuzzy systems. Five attributes are used considered as the inputs of the fuzzy inference systems to obtain a Diabetes diagnosis. The instances are divided into design and testing sets, where the design set allows the genetic algorithm to minimize the error of classification, and finally, the real behavior of the fuzzy inference system is validated with the testing set.

HbA1c for Diabetes Diagnosis Now Mainstream

2010 ◽  
Vol 43 (5) ◽  
pp. 47
Author(s):  
SHERRY BOSCHERT
Keyword(s):  
Diabetes Diagnosis

Midlife Diabetes Diagnosis Doubles Dementia Risk

2009 ◽  
Vol 42 (5) ◽  
pp. 17
Author(s):  
HEIDI SPLETE
Keyword(s):  
Diabetes Diagnosis ◽  
Dementia Risk

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy
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