A novel hybrid intelligent system with missing value imputation for diabetes diagnosis

Ramezani, Rohollah; Maadi, Mansoureh; Khatami, Seyedeh Malihe

doi:10.1016/j.aej.2017.03.043

Cited by 40 publications

(20 citation statements)

References 19 publications

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“…In [ 31 ], a powerful intelligent diagnostic system for diabetics is presented from the PIMA Indian dataset based on a hybrid algorithm called Logistic Adaptive Network-based Fuzzy Inference System (LANFIS). The accuracy of the proposed methodology is relatively insufficient, estimated at 88.03% and its computer complexity is high.…”

Section: Related Workmentioning

confidence: 99%

Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm

Ju-sheng

Safara

2021

Neural Process Lett

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Artificial intelligence is a future and valuable tool for early disease recognition and support in patient condition monitoring. It can increase the reliability of the cure and decision making by developing useful systems and algorithms. Healthcare workers, especially nurses and physicians, are overworked due to a massive and unexpected increase in the number of patients during the coronavirus pandemic. In such situations, artificial intelligence techniques could be used to diagnose a patient with life-threatening illnesses. In particular, diseases that increase the risk of hospitalization and death in coronavirus patients, such as high blood pressure, heart disease and diabetes, should be diagnosed at an early stage. This article focuses on diagnosing a diabetic patient through data mining techniques. If we are able to diagnose diabetes in the early stages of the disease, we can force patients to stay home and care for their health, so the risk of being infected with the coronavirus would be reduced. The proposed method has three steps: preprocessing, feature selection and classification. Several combinations of Harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. The combinations have not examined before for diabetes diagnosis applications. K-nearest neighbor is used for classification of the diabetes dataset. Sensitivity, specificity, and accuracy have been measured to evaluate the results. The results achieved indicate that the proposed method with an accuracy of 91.65% outperformed the results of the earlier methods examined in this article.

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Section: Related Workmentioning

confidence: 99%

Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm

Ju-sheng

Safara

2021

Neural Process Lett

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“…Further, an Adaptive Network-based Fuzzy System (ANFS) and Levenberg-Marquardt Algorithm (LMA)-based solution achieved an 82.3% prediction accuracy [23]. Rohollah et al report on a Logistic Adaptive Network-based fuzzy system with 88% predictive accuracy [5] and Kemal et al [24] developed a Least Square Support Vector Machine (LS-SVM) and Generalization Discriminant Analysis (GDA)-based cascade learning system. A k-means clustering approach reported by Bankat et al [7] successfully eliminates incorrect samples from the dataset.…”

Section: Related Researchmentioning

confidence: 99%

“…A range of Machine Learning (ML) methods such as Logistic Adaptive Networkbased Fuzzy Inference System (LANFIS) [5], Q-learning Fuzzy ARTMAP (FAM), Genetic Algorithm (GA) (QFAM-GA) [6], Hybrid Prediction Model (HPM) [7], Artificial Neural Network (ANN), and Bayesian Networks (BN) (ANN-BN) [8] have been used to develop algorithms for the classification of DB [9,10]. However, reported machine learning-based solutions have been limited in the accuracy of prediction, owing primarily to the lack of the required scope and volume of data for the training and testing of models.…”

Section: Introductionmentioning

confidence: 99%

A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

et al. 2021

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A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.

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“…The diagnosis system achieved 82.3% accuracy. Rohollah et al [11] developed a Logistic Adaptive Network-Based Fuzzy Inference Diagnosis system applied samples with miss values and obtained 88.05% accuracy. Humar et al [12] proposed a hybrid Neural Network System that was developed using Artificial Neural Network and Fuzzy Neural Network for diagnosis of DBD and obtained accuracy 79.16 %.…”

Section: Introductionmentioning

confidence: 99%

A New Intelligent Approach for Effective Recognition of Diabetes in the IoT E-HealthCare Environment

Au¹,

Li²,

Khan³

et al. 2020

Preprint

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A significant attention has been made to the accurate detection of diabetes which is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the IoT e-healthcare environment. Internet of Things (IOT) has emerging role in healthcare services which delivers a system to analyze the medical data for diagnosis of diseases applied data mining methods. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a IOT based diagnosis system using machine learning methods, such as preprocessing of data, feature selection, and classification for the detection of diabetes disease in e- healthcare environment. Model validation and performance evaluation metrics have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning Decision Tree algorithms, such as Ada Boost and Random Forest are also used for feature selection and compared the classifier performance with wrapper based feature selection algorithms also. Machine learning classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the Decision Tree algorithm based on selected features improves the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high as compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and GL, DPF, and BMI are more significantly important features in the dataset for prediction of diabetes disease. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would be effectively detected diabetes disease and can easily be deployed in IOT wireless sensor technologies based e-healthcare environment.

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A novel hybrid intelligent system with missing value imputation for diabetes diagnosis

Cited by 40 publications

References 19 publications

Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm

Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm

A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

A New Intelligent Approach for Effective Recognition of Diabetes in the IoT E-HealthCare Environment

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