Two-Stage Classifier for Diagnosis of Hypertension Type

Woźniak, Michał

doi:10.1007/11946465_39

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…Machine learning (ML) methods are an attractive solution for such a task as they offer fast and precise intelligent analysis of multidimensional data. Such algorithms are widely used for clinical decision support [17] and are applied by authors to the tasks as the hypertension diagnosis [18], drug discovery [19], nephropathy detection among new-borns [20], or abdominal pain diagnosis [31].…”

Section: Introductionmentioning

confidence: 99%

Automatic diagnosis of primary headaches by machine learning methods

Krawczyk

Simić

et al. 2013

Open Medicine

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AbstractPrimary headaches are common disease of the modern society and it has high negative impact on the productivity and the life quality of the affected person. Unfortunately, the precise diagnosis of the headache type is hard and usually imprecise, thus methods of headache diagnosis are still the focus of intense research. The paper introduces the problem of the primary headache diagnosis and presents its current taxonomy. The considered problem is simplified into the three class classification task which is solved using advanced machine learning techniques. Experiments, carried out on the large dataset collected by authors, confirmed that computer decision support systems can achieve high recognition accuracy and therefore be a useful tool in an everyday physician practice. This is the starting point for the future research on automation of the primary headache diagnosis.

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Section: Introductionmentioning

confidence: 99%

Automatic diagnosis of primary headaches by machine learning methods

Krawczyk

Simić

et al. 2013

Open Medicine

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“…As a consequence, individuals may easily disregard the occurrence of hypertension and develop potential serious complications (Vasan et al, 2001). Though is among the most common and costly health problems, it is also among the most preventable and can be effectively controlled through reasonable measures due to the fact that lifestyle choices are linked to the occurrence and development of hypertension (Chang, Wang, & Jiang, 2011;Hsu et al, 2011;Krawczyk & Wozniak, 2011;Sumathi & Santhakumaran, 2011;Wozniak, 2006). Therefore, investigating risk factors and identifying hypertension plays a crucial role in the effective prevention and reduction of the onset of cardiovascular diseases as well as better management and intervention of individual health conditions (Hsu et al, 2011).…”

Section: Introductionmentioning

confidence: 98%

Predicting hypertension without measurement: A non-invasive, questionnaire-based approach

Wang

Chen

et al. 2015

Expert Systems with Applications

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“…Since lifestyle behaviors such as drinking, smoking habits and physical activity level contribute to the occurrence and development of hypertension, many researchers have conducted studies in the construction of hypertension diagnosis and prediction model by integrating behavior risk factors and demographics such as age, sex, height and weight with clinical laboratory data [2,4,11,12,13,14]. Compared with clinical laboratory data, anthropometric body surface scanning data and genomic data, behavior information are easily collected and meaningful in the prevention and management of hypertension and more suitable for use in a large population.…”

Section: Introductionmentioning

confidence: 99%

A Logistic Regression and Artificial Neural Network-Based Approach for Chronic Disease Prediction: A Case Study of Hypertension

Wang

Xia

et al. 2014

2014 IEEE International Conference on Internet of Things(iThings), and IEEE Green Computing and Communications (GreenCom) and I

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The global trend of population aging and the continuing maturity of the Internet of Things (IoT) technology drives the rapid development of health care. In the comprehensive applications of IoT technology, developing and constructing a prediction model for chronic diseases is a great improvement to healthcare technology as well as an exploration of IoT technology on the data-analysis and decision-making level. Considering that early detection, diagnosis and screening of hypertension plays a significant role in the prevention and reduction of the onset of cardiovascular diseases as well as the improvement of quality of life, it is of great value to figure out hypertension-related risk factors and further establish a model for the prediction of hypertension with the identified risk factors. Thus, in this paper, we put forward to integrate logistic regression analysis and Artificial Neural Networks (ANNs) model for the selection of risk factors and the prediction of chronic diseases by taking a case study of hypertension. First, binary logistic regression model was applied on experimental dataset collected from Behavior Risk Factor Surveillance System (BRFSS) to select factors statistically significant to hypertension in terms of the predefined p-value. Then, a Multi-Layer Perception (MLP) neural network model with Back Propagation (BP) algorithm was constructed and trained for the prediction of hypertension with the selected risk factors as inputs to ANNs. Experimental results showed that our proposed approach achieved more than 72% prediction accuracy acceptable in the diagnosis of hypertension and that the Area Under the receiver-operator Curve (AUC) was more than 0.77. The results indicate that integration of logistic regression and artificial neural networks provides us an effective method in the selection of risk factors and the prediction of hypertension, as well as a general approach for the prediction of other chronic diseases.

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Two-Stage Classifier for Diagnosis of Hypertension Type

Cited by 8 publications

References 8 publications

Automatic diagnosis of primary headaches by machine learning methods

Automatic diagnosis of primary headaches by machine learning methods

Predicting hypertension without measurement: A non-invasive, questionnaire-based approach

A Logistic Regression and Artificial Neural Network-Based Approach for Chronic Disease Prediction: A Case Study of Hypertension

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