A predictive model for cerebrovascular disease using data mining

Yeh, Duen-Yian; Cheng, Ching‐Hsue; Chen, Yen‐Wen

doi:10.1016/j.eswa.2011.01.114

Cited by 76 publications

(40 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, effective applications of artificial neural network (ANN) and logistic regression to predict medical outcomes have been proved in many works [25,27,28,31]. However, a challenge remains still in securing an effective analytic tool with high accuracy to help make evidence-based decisions with quality efficiently.…”

Section: Related Workmentioning

confidence: 99%

“…Excessive efforts have been done to assess and predict chronical diseases using clinical decision support systems, for example, trying to predict heart disease at an early stage [25].…”

Section: Introductionmentioning

confidence: 99%

“…Many of these researches have been carried out in data mining and analytic on medical data. The assessment and prediction of various diseases for patients have been widely reviewed by using data mining techniques and statistical tools [4,5,6,7,8,25]. Especially, there have been a number of the predictive models which employ artificial neural network and regressions for predicting medical outcomes in various chronical diseases [25,27,28,31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An intelligent recommender system based on predictive analysis in telehealthcare environment

Lafta

Zhang

Tao

et al. 2016

WEB

View full text Add to dashboard Cite

Abstract. The use of intelligent technologies for providing useful recommendations to patients suffering chronic diseases may play a positive role in improving the general life quality of patients and help reduce the workload and cost involved in their daily healthcare. The objective of this study is to develop an intelligent recommender system based on predictive analysis for advising patients in the telehealth environment concerning whether they need to take the body test one day in advance by analyzing medical measurements of a patient for the past k days. The proposed algorithms supporting the recommender system have been validated using a time series telehealth data recorded from heart disease patients which were collected from May to January 2012, from our industry collaborator Tunstall. The experimental results show that the proposed system yields satisfactory recommendation accuracy and offer a promising way for saving the workload for patients to conduct body tests every day. This study highlights the possible usefulness of the computerized analysis of time series telehealth data in providing appropriate recommendations to patients suffering chronic diseases such as heart diseases patients.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Excessive efforts have been done to assess and predict chronical diseases using clinical decision support systems, for example, trying to predict heart disease at an early stage [25].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An intelligent recommender system based on predictive analysis in telehealthcare environment

Lafta

Zhang

Tao

et al. 2016

WEB

View full text Add to dashboard Cite

show abstract

“…The framework is outlined in light of informational indexes of Cleveland and Hungarian coronary illness. Yeh et al [10] studied that acquired 493 legitimate examples from expectation and conduct programs that cerebro vascular ailment and embraced three order calculations, decision trees, Bayesian classifier and BP neural system, to construct an arrangement demonstrate individually. Hand et al [11] explained that artificial neural network is a highly parametric statistical model has attracted considerable attention in recent years.…”

Section: Review Of Literaturementioning

confidence: 99%

Data Mining Techniques to Construct a Model: Cardiac Diseases

Akhtar¹,

Talib²,

Kanwal³

2018

ijacsa

View full text Add to dashboard Cite

Abstract-Using echocardiography flexible TransthoracicEchocardiography reported data set detecting heart disease by using mining techniques designed prediction model the data set can develop the reliability of analysis of cardiac diseases by echocardiography, using eight iterative and interactive steps consisting Knowledge Discovery in Database (KDD) methodology including from 209 patients with echocardiography to extracting the data important mode of action Transthoracic Echocardiography inspection report. This study used data from Faisalabad Institute of Cardiology study from 2012 to 2015. All models exposed the results of J48 decision tree, naïve bayes classifier and neural network that has extraordinary classification precision and predictive of heart disease cases are generally comparable. However, J48 model predictive classification accuracy shows of 80% based on the true positive rate ratio and performance slightly better. This study shows to predict heart disease cases and People can be used the results of our study to make more consistent diagnosis of cardiac disease and to help them as a support tool for cardiac disease specialists.

show abstract

“…The techniques used can be broadly classified as descriptive and predictive models. Descriptive models are used to identify patterns in data and the relationships between factors responsible for them [21]. Descriptive analytics involve methods such as clustering, summarizations, association rules, and sequence analysis [22].…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Recommender System Based on Short-Term Risk Prediction for Heart Disease Patients

Lafta

Zhang

Tao

et al. 2015

2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

View full text Add to dashboard Cite

Abstract-In this paper, an intelligent recommender system is developed, which uses an innovative time series prediction algorithm to provide recommendations to heart disease patients in the tele-health environment. Based on analytics of each patient's medical tests in records, the system provides the patient with decision support for necessity of medical tests. The experimental results show that the proposed system yields satisfactory accuracy in recommendations. The system also offers a promising way for saving the workload for patients and healthcare practitioners in conducting daily medical tests. The research will help reduce the workload and cost in healthcare and help the healthcare industry transform from the traditional scenario to more a personalized paradigm in a tele-health environment.

show abstract

A predictive model for cerebrovascular disease using data mining

Cited by 76 publications

References 19 publications

An intelligent recommender system based on predictive analysis in telehealthcare environment

An intelligent recommender system based on predictive analysis in telehealthcare environment

Data Mining Techniques to Construct a Model: Cardiac Diseases

An Intelligent Recommender System Based on Short-Term Risk Prediction for Heart Disease Patients

Contact Info

Product

Resources

About