GA_RBF NN: a classification system for diabetes

Choubey, Dilip Kumar; Paul, Sanchita

doi:10.1504/ijbet.2017.10003045

Cited by 6 publications

(8 citation statements)

References 28 publications

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“…Experimentally, the initial seed point value resulted in the modification. Choubey et al [ 24 ] utilized J48, random forest, and ANN for classification and utilising unsupervised techniques like principal component analysis (PCA) after feature reduction.…”

Section: Related Workmentioning

confidence: 99%

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Padhy

Dash

Routray

et al. 2022

Computational Intelligence and Neuroscience

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Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early. The main purpose of this work is to develop enhanced diabetes management applications which help in patient monitoring and technology-assisted decision-making. The proposed hybrid ensemble ML model predicts diabetes mellitus by combining both bagging and boosting methods. An online IoT-based application and offline questionnaire with 15 questions about health, family history, and lifestyle were used to recruit a total of 10221 people for the study. For both datasets, the experimental findings suggest that our proposed model outperforms state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Padhy

Dash

Routray

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Table 3 provides an outlook of recent advances of machine learning algorithms with optimizers in diabetes prediction. From the table we can see [34]- [44] have utilized several optimization techniques to enhance the overall prediction accuracy. Around all recent works have utilized Pima Indians Diabetes Database (PIDD).…”

Section: Discussion Challenges and Future Directionsmentioning

confidence: 99%

“…As per the literatures, the dataset splitting is done manually. On the other hand, in [44], Enhanced and Adaptive Genetic Algorithm (EAGA) based optimization also achieved a higher accuracy of 94.7%. Most of the accuracy ranging from 76% to 99%.…”

Section: Discussion Challenges and Future Directionsmentioning

confidence: 99%

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Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

et al. 2021

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Artificial intelligence is a spectacular part of computer engineering that has earned a compelling diversion in the field of medical data classification due to its state-of-art algorithmic strength and learning capabilities. Machine Learning is a major sub-domain of artificial intelligence, where it has become one of most promising fields in computer science. In recent years, there is a large spectrum of healthcare and biomedical data has been growing intensely. Due to the huge labeled or unlabeled data, it is important to have a compact and robust machine learning solution for classification. Several optimizers have been deployed to improve the inclusive performance of machine learning models. The classification of machine learning models depend on several factors. This comprehensive review paper aims to insight the current stage of optimized machine learning success on medical data classification. Increasing number of unstructured medical data has been utilizing in machine learning algorithms to predict intuitions. But it is difficult to inherent immense intuition from those data. So machine learning researchers have utilized stateof-art optimizers and novel feature selection techniques to overcome and emend the performance accuracy. We have highlighted some recent literatures, which exhibits the robust impact of optimizers and feature selection on machine learning techniques on medical data characterization. On the other hand, a cleancut introduction on machine learning and theoretical outlook of widely utilized optimization techniques like genetic algorithm, gray wolf optimization, and particle swarm optimization are discussed for initial understanding to the optimization techniques.

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“…Paul and Choubey [25] introduced a twofold scheme for diabetes classification where the first stage develops genetic algorithm model for feature selection which reduces dimension from 8 to 4. In next stage, radial basis function neural network (RBF NN) model is implemented for classification of the final attribute set.…”

Section: Background Workmentioning

confidence: 99%

A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory

Kumari

Padmaja²,

Suma

2022

IJEECS

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Hyperglycemia arises due to diabetes mellitus, which is a persistent and life-threatening ailment. In this paper deep convolution neural network can be embedded to long short-term memory networks to recognize early prediction of diabetes and to decrease the complications that can be occurred through diabetes irrespective to the age. Diabetes problem is being gradually growing and presently, it is reported as a significant cause of death in the top spot. According to the recent studies 48% of overall world population will be affected by diabetes by 2045. If diabetes unidentified in early stages, it may cause other additional cardiac problems. In the proposed based work, a deep learning framework deep combination of convolution neural network and long short-term memory is proposed by embedding both to leverage their respective advantages for diabetes recognition and to allow early prediction of diabetes to avoid other complications. The experimental evolution on the bunch mark of diabetes data set demonstrates the proposed model embedded deep long short-term memory outperforms other machine learning and conventional deep learning approaches. The proposed algorithm in this paper outperforms existing techniques and evaluates total effectiveness and accuracy of predicting whether a person will suffer from diabetes.

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GA_RBF NN: a classification system for diabetes

Cited by 6 publications

References 28 publications

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory

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