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

Chen, Tianhua; Shang, Changjing; Su, Pan; Antoniou, Grigoris; Shen, Qiang

doi:10.1007/978-3-319-97982-3_19

Cited by 7 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kalaiselvi C, Nasira GM [16] proposed an approach for diagnosis of both diabetes and cancer using ANFIS with adaptive group based KNN, and they achieved 80.00% accuracy on PIMA dataset. Tianhua Chen, Changjing Shang et al [17] proposed an effective diagnosis approach of diabetes with ANFIS initialized with decision tree, and achieved 75.67% accuracy on PIMA dataset.…”

Section: Related Workmentioning

confidence: 99%

A Revised Adaptive Network-based Fuzzy Inference System Combined with Neural Network to Predict Diabetes

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

Today, neural network models are widely used to predict whether a person will develop diabetes in the future. However, for fuzzy inference engine and Adaptive Network-based Fuzzy Inference System (ANFIS), it costs a lot when the number of features is large, and the model will be more explainable if we use feature selection methods. In this paper, we modify the ANFIS model, combine ANFIS and neural network, and propose the ANFIS-NN model. We use SMOTE to address the imbalance between different classes, and use RFE and Casual Inference to do feature selection work. Then, we train an ANFIS model, and use a 5-layer neural network to replace the last layers to improve prediction accuracy. Data comparison experiments shows that our models get 0.9812 on accuracy, 0.9790 on G-mean, 0.9776 on F1 score on PIMA dataset using Casual Inference feature selection method, much better than traditional ANFIS; and our model works better on other diabetes datasets. The ANFIS-NN proposed in this paper can also be applied to other datasets to predict diabetes.

show abstract

Section: Related Workmentioning

confidence: 99%

A Revised Adaptive Network-based Fuzzy Inference System Combined with Neural Network to Predict Diabetes

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The system could have been further improved by including different age groups. Chen et al [ 31 ] proposed a method based on the Takagi-Sugeno-Kang (TSK) fuzzy rule for the diagnosis of diabetes. The proposed method began with the creation of a crisp rule base using a decision tree, a mechanism capable of learning fundamental rules that represent the relationships between domain input and output attributes with low overhead.…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy Rule-Based System for Classification of Diabetes

Aamir

Sarfraz

Ramzan

et al. 2021

Sensors

View full text Add to dashboard Cite

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

show abstract

“…Recent advance in machine learning has enjoyed a number of successes in medical applications [16] [17]. To address this challenge, we wanted to investigate if there was a way by which using clinical information collected from a Service which delivers clinical pathway which is compliant with NICE recommendations (i.e.…”

Section: Introductionmentioning

confidence: 99%

Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Machine Learning

Chen

Antoniou

Adamou

et al. 2021

Applied Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that includes symptoms such as inattentiveness, hyperactivity and impulsiveness. It is considered as an important public health issue, and prevalence of diagnosis has increased as awareness of the disease grew over the past years. Supply of specialist medical experts has not kept pace with the increasing demand for assessment, both due to financial pressures on health systems and the difficulty to train new experts, resulting in growing waiting lists. Patients are not being treated quickly enough causing problems in other areas of health systems (e.g. increased GP visits, increased risk of self-harm and accidents) and more broadly (e.g. time off work, relationship problems). Advances in machine learning make it possible to attempt to diagnose ADHD based on the analysis of relevant data, and this could inform clinical practice. This paper reports on findings related to the mental health services of a specialist Trust within the UK's National Health Service (NHS). The analysis studied data of adult patients who underwent diagnosis over the past few years, and developed a diagnostic model for ADHD in adults. The results demonstrate that it is indeed possible to correctly diagnose ADHD patients with promising statistical accuracy.

show abstract

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

Cited by 7 publications

References 18 publications

A Revised Adaptive Network-based Fuzzy Inference System Combined with Neural Network to Predict Diabetes

A Revised Adaptive Network-based Fuzzy Inference System Combined with Neural Network to Predict Diabetes

A Fuzzy Rule-Based System for Classification of Diabetes

Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Machine Learning

Contact Info

Product

Resources

About