DDO: a diabetes mellitus diagnosis ontology

El–Sappagh, Shaker; Ali, Farman

doi:10.1186/s40535-016-0021-2

Cited by 46 publications

(32 citation statements)

References 52 publications

(84 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some features, such as patient diseases, require a unification of terminology for medical terms. We used the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) standard terminology to standardize and unify these terms [50].…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Domain experts participated in this process with the help of some of the most recent diabetes CPGs [60,61]. In another study, authors created a standard diabetes diagnosis ontology in Web Ontology Language 2 (OWL 2) format, which deeply studies this issue [50]. According to the most recent CPGs, a diabetes diagnosis cannot be made by only conducting lab tests for glucose levels.…”

Section: Domain and Data Understandingmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

et al. 2019

Self Cite

View full text Add to dashboard Cite

Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to diagnose DM by optimally employing multiple classifiers based on bagging and random subspace techniques. The proposed framework combines seven of the most suitable and heterogeneous data mining techniques, each with a separate set of suitable features. These techniques are k-nearest neighbors, naïve Bayes, decision tree, support vector machine, fuzzy decision tree, artificial neural network, and logistic regression. The framework is designed accurately by selecting, for every sub-dataset, the most suitable feature set and the most accurate classifier. It was evaluated using a real dataset collected from electronic health records of Mansura University Hospitals (Mansura, Egypt). The resulting framework achieved 90% of accuracy, 90.2% of recall = 90.2%, and 94.9% of precision. We evaluated and compared the proposed framework with many other classification algorithms. An analysis of the results indicated that the proposed ensemble framework significantly outperforms all other classifiers. It is a successful step towards constructing a personalized decision support system, which could help physicians in daily clinical practice.

show abstract

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Domain and Data Understandingmentioning

confidence: 99%

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…--- (2) Support vector machine SVM is one of the supervised learning algorithms in machine learning area. It is used to analyze the records which is used for classification and regression analysis.…”

Section: Entropy(s)mentioning

confidence: 99%

“…It is the most common one caused when body cells stop responding to insulin's produced in the body. Type -2 diabetes has significantly increase in the current time, according to International Diabetic Federation Projection of prevalence of diabetic is expected to reach 471 million by 2035, that means one of ten people will be suffering from diabetic [1,2]. Research has proved that diabetic is a root cause for many other diseases like, Diabetic Retinopathy, Macro Vascular Complications, Nephropathy, Neuropathy.…”

Section: Introductionmentioning

confidence: 99%

“…DM identification metaphysics provide a typical ontology which will sustain ability among clinical call support system and attention systems. It's in an exceedingly one amongst in every sickness metaphysics residential to signify numerous ailment aspect in typical coherent form [2]. Decision Tree plays a major role in prediction by using classification and regression methodology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Ontology Driven System to Predict Diabetes With Machine Learning Techniques

H R*,

Ramesh,

Prakash

2019

IJITEE

View full text Add to dashboard Cite

Diabetes Mellitus is considered one of the chronic diseases of humankind which causes an increase in blood sugar. Many complications are reported if DM remains untreated and unidentified. Identification of this disease requires a lot of physical and mental trauma and effort which involves visiting a doctor, blood and urine test at the diagnostic center which consumes more time. Difficulties can be over crossed using the trending technology of Machine learning. The idea of the model is to prognosticate the occurrence of a diabetic with high accuracy. Therefore, two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage. Therefore two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage.

show abstract

Role of Ontology in Health Care

Singla

2020

Ontology‐Based Information Retrieval for Healthcare Systems

View full text Add to dashboard Cite

DDO: a diabetes mellitus diagnosis ontology

Cited by 46 publications

References 52 publications

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

An Ontology Driven System to Predict Diabetes With Machine Learning Techniques

Role of Ontology in Health Care

Contact Info

Product

Resources

About