Different medical data mining approaches based prediction of ischemic stroke

Arslan, Ahmet; Çolak, Cemil; Sarıhan, Mehmet Ediz

doi:10.1016/j.cmpb.2016.03.022

Cited by 80 publications

(37 citation statements)

References 16 publications

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“…The fundamental approach for data mining is to extract underlying fruitful information from the enormous pile of data records and then transform it into an understandable outcome for future use. In the past, several predictive systems for medical data analysis had been introduced, which possess categorization and forecast of medical records . After the exploration of knowledge from the obtained data machine learning process is executed, in particular, training phase will be started by which intelligent decisions were trained to make in future based on the sample training data .…”

Section: Introductionmentioning

confidence: 99%

“…In the past, several predictive systems for medical data analysis had been introduced, which possess categorization and forecast of medical records. 7,8 After the exploration of knowledge from the obtained data machine learning process is executed, in particular, training phase will be started by which intelligent decisions were trained to make in future based on the sample training data. 9 The machine learning process used in this step can be categorized into two such as supervised learning and unsupervised learning based processes depending on data availability.…”

Section: Introductionmentioning

confidence: 99%

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Clustering‐based approach for medical data classification

Kodabagi

Tikotikar

2018

Concurrency and Computation

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Summary Medical data records are growing enormously in present days due to the growth of various medical technologies and population of the globe. It is a difficult task for a medical expert to process these data records to identify and provide treatment regarding the disease of patient. Hence, it is necessary to automate the processing of such medical data using machine learning‐based Medical Decision Support System. In this proposed work, an Effective Fuzzy Rule Classifier (EFRC)–based decision support system is used for analysis of UCI medical dataset for identification of disease of patients. Initially, the method determines the best centroid value for each of the attribute using training dataset. Furthermore, the centroid value is refined using test samples. The refined centroid values of all the attributes are used by the fuzzy classifier for medical data classification. Experimental results have proven that EFRC performs better classification than existing systems in terms of accuracy, sensitivity, and specificity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering‐based approach for medical data classification

Kodabagi

Tikotikar

2018

Concurrency and Computation

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“…Ahmet K. Arslan et al [20] used three data mining algorithms, namely: Support Vector Machine (SVM), Stochastic Gradient Boosting (SGB) and penalized logistic regression (PLR) to predict stroke. SVM achieved an accuracy of 98% [20]. In addition, by using K-nearest neighbor and C4.5 decision tree, Leila Amini et al [18] achieved an accuracy of stroke prediction equal to 94.2% and 95.4% respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In their study, they used K-nearest neighbor model, multiple linear regression, and regression tree model, that resulted an accuracy of 0.743, 0.742, and 0.737, with 95% confidential interval [19]. Ahmet K. Arslan et al [20] used three data mining algorithms, namely: Support Vector Machine (SVM), Stochastic Gradient Boosting (SGB) and penalized logistic regression (PLR) to predict stroke. SVM achieved an accuracy of 98% [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Stroke using Data Mining Classification Techniques

Almadani¹,

Alshammari²

2018

ijacsa

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Abstract-Stroke is a neurological disease that occurs when a brain cells die as a result of oxygen and nutrient deficiency. Stroke detection within the first few hours improves the chances to prevent complications and improve health care and management of patients. In addition, significant effect of medications that were used as treatment for stroke would appear only if they were given within the first three hours since the beginning of stroke. A framework has been designed based on data mining techniques on Stroke data set that is obtained from Ministry of National Guards Health Affairs hospitals, Kingdom of Saudi Arabia. A data mining model was built with 95% accuracy. Furthermore, this study showed that patient with the following medical conditions, such as heart diseases (hypertension mainly), immunity diseases, diabetes militias, kidney diseases, hyperlipidemia, epilepsy, or blood (platelets) disorders has a higher probability to develop stroke.

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“…Medical data analysis is important due to its meaning for medical decision making and diagnosis [2], [3]. Many studies have been conducted on the topic of classification techniques and how to improve their performance [4]- [6], specifically when it comes to the treatment of imbalanced datasets, but no universal, highly performing solution has been discovered yet.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing compensation techniques for improved classification of imbalanced medical datasets

Wosiak

Karbowiak

2017

Annals of Computer Science and Information Systems

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Abstract-The paper describes the study on the problem of applying classification techniques in medical datasets with a class imbalance. The aim of the research is to identify factors that negatively affect classification results and propose actions that may be taken to improve the performance. To alleviate the impact of uneven and complex class distribution, methods of balancing the datasets are proposed and compared. The experiments were conducted on five datasets -three binary and two multiclass. They comprise several data preprocessing methods applied on data and the classification with different techniques. The study shows that for some datasets there exists a combination of a certain preprocessing method and a classification technique which outperforms other approaches. For datasets with complex distribution or too many features the ratio of correctly predicted labels may be low regardless what resampling method and classification technique has been applied.

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Different medical data mining approaches based prediction of ischemic stroke

Cited by 80 publications

References 16 publications

Clustering‐based approach for medical data classification

Clustering‐based approach for medical data classification

Prediction of Stroke using Data Mining Classification Techniques

Preprocessing compensation techniques for improved classification of imbalanced medical datasets

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