A rule extraction approach from support vector machines for diagnosing hypertension among diabetics

Singh, Namrata; Singh, Pradeep; Bhagat, Deepika

doi:10.1016/j.eswa.2019.04.029

Cited by 47 publications

(23 citation statements)

References 55 publications

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“…Singh et al proposed a new method called rule extraction from the support vector machine to diagnose hypertension in diabetes mellitus patients. And then, they achieved excellent results in the classification of hypertension types in people having diabetes mellitus [7]. In another work [8], Abdullah et al proposed a fuzzy expert system (FES) to diagnose hypertension in male and female patients of age groups 10, 20, 30, and 40. ey modeled the hypertension cases for each age group based on the FES model [8].…”

Section: Related Workmentioning

confidence: 99%

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Polat

2020

Mathematical Problems in Engineering

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Hypertension (high blood pressure) is an important disease seen among the public, and early detection of hypertension is significant for early treatment. Hypertension is depicted as systolic blood pressure higher than 140 mmHg or diastolic blood pressure higher than 90 mmHg. In this paper, in order to detect the hypertension types based on the personal information and features, four machine learning (ML) methods including C4.5 decision tree classifier (DTC), random forest, linear discriminant analysis (LDA), and linear support vector machine (LSVM) have been used and then compared with each other. In the literature, we have first carried out the classification of hypertension types using classification algorithms based on personal data. To further explain the variability of the classifier type, four different classifier algorithms were selected for solving this problem. In the hypertension dataset, there are eight features including sex, age, height (cm), weight (kg), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), heart rate (bpm), and BMI (kg/m2) to explain the hypertension status and then there are four classes comprising the normal (healthy), prehypertension, stage-1 hypertension, and stage-2 hypertension. In the classification of the hypertension dataset, the obtained classification accuracies are 99.5%, 99.5%, 96.3%, and 92.7% using the C4.5 decision tree classifier, random forest, LDA, and LSVM. The obtained results have shown that ML methods could be confidently used in the automatic determination of the hypertension types.

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Section: Related Workmentioning

confidence: 99%

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Polat

2020

Mathematical Problems in Engineering

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“…We used the type 1 diabetes mellitus, 35 type 2 diabetes mellitus, 36 and GDM 37 datasets, which were obtained from previously published literature [38][39][40][41][42][43][44][45][46][47][48] for training, testing, and validation. More details about the data are shown in Table 2.…”

Section: Data Collectionmentioning

confidence: 99%

“…This information utilized by different research in Type-1 Diabetes. [38][39][40] This information presented new methodology of hazard factor forecast and discovering the importance level among factors like subcomponents. Investigated Dataset of both Data Mining and Statistical methodology delineates the correlation impact and reasonable result of the exploration.…”

Section: Data Collectionmentioning

confidence: 99%

Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification

Nagaraj¹,

Deepalakshmi²,

Mansour³

et al. 2021

DMSO

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Purpose Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to classify the three diabetes type diagnoses according to multiple patient attributes. Methods Three different datasets are used to develop a novel medical data classification model. The proposed model involved preprocessing, artificial flora algorithm (AFA)-based feature selection, and gradient boosted tree (GBT)-based classification. Then, the processing occurred in two steps, namely, format conversion and data transformation. AFA was applied for selecting features, such as demographics, vital signs, laboratory tests, medications, from the patients’ electronic health records. Lastly, the GBT-based classification model was applied for classifying the patients’ cases to type I, type II, or gestational diabetes mellitus. Results The effectiveness of the proposed AFA-GBT model was validated using three diabetes datasets to classify patient cases into one of the three different types of diabetes. The proposed model showed a maximum average precision of 91.64%, a recall of 97.46%, an accuracy of 99.93%, an F-score of 94.19%, and a kappa of 96.61%. Conclusion The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.

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“…In case of bio medical data such as cancer data, diabetes data limited algorithms are presented by researchers in this era. A modified version of support vector machine was presented by researcher in literature [6] analyzing the diabetics and hypertension. Similarly a hybrid model is proposed in literature [7] for diagnosing the clinical data of patients.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Data Mining Model for Diabetic Patients Data Using Hybrid Adaptive Neuro- Fuzzy Inference System

Abinesh*,

Prabakaran,

Arunkumar

2019

IJEAT

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Proper diagnosis of diabetic based on the patient’s medical analysis results is an important factor. Data mining helps in analyzing such data includes complex meaningful terms to diagnosis and supports the patients to take remedy action based on the accurate results. The proposed model is a data mining model for analyzing diabetic patient’s data using sugeno type adaptive neuro fuzzy inference system with principle component analysis as a hybrid system. The experimental model validated through 200 different data obtained from health clinic with 25 different attributes. The proposed model classifies the data with accuracy of 94.6% where as conventional rough set and k means clustering model produces less classification accuracy of 74.5% and 77.6%.

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A rule extraction approach from support vector machines for diagnosing hypertension among diabetics

Cited by 47 publications

References 55 publications

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification

An Intelligent Data Mining Model for Diabetic Patients Data Using Hybrid Adaptive Neuro- Fuzzy Inference System

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