Machine learning techniques for classification of diabetes and cardiovascular diseases

Alić, Berina; Gurbeta, Lejla; Badnjević, Almir

doi:10.1109/meco.2017.7977152

Cited by 110 publications

(25 citation statements)

References 19 publications

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“…The proposed system has been tested on the diabetes data set which is a clinical vital data set and designed from clinical obervations [8]. Additionally, the performances of the proposed method have been compared with the state of the art methods, such as LANFIS [9], TSHDE [10], C4.5 algorithms [11], Modified K-Means Clustering + SVM (10-FC) [12] and BN [13]. The experimental results demonstrated that the proposed method Filter based (DT-(ID3) +DT) achieved high classification accuracy compared with previous methods.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data

Haq

Khan

et al. 2020

Sensors

115

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Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time 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 algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high 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 Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.

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Section: Introductionmentioning

confidence: 99%

Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data

Haq

Khan

et al. 2020

Sensors

115

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“…The use of artificial neural networks (ANN) and Bayesian Network (BN) to classify diabetes and cardiovascular disease is studied. Using the Levenberg-Marquardt learning algorithm (multilayer feed-forward neural network) as an ANN technique is incorporated to conclude the hypothesis of higher acquirement of reliable statistics in classifying diabetes and heart disease diagnosis [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Cardiovascular Disease Prediction Empowered with Gradient Descent Optimization

Nawaz

Shoaib

Ashraf

2021

Heliyon

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Disorders of the heart and blood vessels are named cardiovascular disease. 'The heart's proper functionality is of an utmost necessity for the survival of life. The death rate due to heart disease, has been increased rapidly. Cardiovascular illness is believed the deadliest cause of death across the globe. From the facts and figures shared by the WHO (World Health Organization) 17.9 Million human lost their lives due to cardiovascular diseases. This research is carried out for the effective diagnosis of heart disease using the heart disease dataset available on the UCI Machine Repository. Heart disease diagnosis with an optimization algorithm can be fruitful in terms of higher accuracy and sensitivity. Finding an acceptable optimal solution among multiple solutions for a specific problem is known as optimization. Different machine learning algorithms have been applied as Support Machine Vector (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Artificial Neural Network (ANN), Random Forest (RF), and Gradient Descent Optimization (GDO). Intelligent Cardiovascular Disease Prediction Empowered with Gradient Descent Optimization model produces the optimal results among under consideration classification algorithms. 98.54 % accuracy has been achieved by the GDO based model while performance evaluation it. 99.43% sensitivity (recall) and 97.76% precision have also been recorded. From the prediction results of the system, it's satisfactory to utilize it for cardiovascular disease diagnosis. The proposed system will be helpful for the analysis of cardiovascular disease.

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“…The proposed system has been tested on the diabetes data set. Additionally, the performances of the proposed method have been compared with the state of the art methods, such as LANFIS [39], SM-Rule-Miner [23], TSHDE [21], C4.5 algorithm [14], Intelligent SVM [38], Modified K-Means Clustering +SVM (10-FC) [40] and BN [41]. The experimental results demonstrated that the proposed method Filter based (DT-(ID3) +DT) achieved high classification accuracy as compared with previous methods.…”

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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Machine learning techniques for classification of diabetes and cardiovascular diseases

Cited by 110 publications

References 19 publications

Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data

Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data

Intelligent Cardiovascular Disease Prediction Empowered with Gradient Descent Optimization

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

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