Stroke is the third most common cause of death and the most common cause of long-term disability among adults around theworld. Therefore, stroke prediction and diagnosis is a very important issue. Data mining techniques come in handy to help determine the correlations between individual patient characterisation data, that is, extract from the medical information system the knowledge necessary to predict and treat various diseases. The study analysed the data of patients with stroke using eight known classification algorithms (J48 (C4.5), CART, PART, naive Bayes classifier, Random Forest, Supporting Vector Machine and neural networks Multilayer Perceptron), which allowed to build an exploration model given with an accuracy of over 88%. The potential features of patients, which may be factors that increase the risk of stroke, were also indicated.
Neurological disorders are diseases of the brain, spine and the nerves that connect them. There are more than 600 diseases of the nervous system, such as epilepsy, Parkinson's disease, brain tumors, and stroke as well as less familiar ones such as multiple sclerosis or frontotemporal dementia. The increasing capabilities of neurotechnologies are generating massive volumes of complex data at a rapid pace. Evaluating and diagnosing disorders of the nervous system is a complicated and complex task. Many of the same or similar symptoms happen in different combinations among the different disorders. This paper provides a survey of developed selected data mining methods in the area of neurological diseases diagnosis. This review will help experts to gain an understanding of how data mining techniques can assist them in neurological diseases diagnosis and patients treatment.
Data mining is an analytical process, which deals with the study of large data sets in search of patterns, correlations between data, and later their evaluation. The goal of data mining is usually prediction, among others sales volume, customer activities, extension ratios or the scale of customer loss. Data mining techniques allow finding previously unknown dependencies and schemas that can be used to support decision making or database description. Data mining techniques are developing very quickly and are more and more often used not only in typical fields such as customer relationship or management, but also in medicine, biomechanics, industry, materials sciences or mechanical engineering. The aim of this work is to evaluate the effectiveness of selected data mining techniques for predicting the concrete compressive strength, and to identify the features having the greatest impact on its compressive strength. The study analyzed the data of 1030 concrete samples using five known classification algorithms (C4.5, Random Forest, Naive Bayes Classifier, Supporting Vector Machine SVM) and neural networks (Multilayer Percepton), which allowed to build an exploration model given with an accuracy of over 99%. Potential features of concrete that may affect its compressive strength are also pointed out.
BACKGROUND: Disability, especially in children, is a very important and current problem. Lack of proper diagnosis and care increases the difficulty for children to adapt to disabilities. Disabled children have many problems with basic activities of daily living. Therefore, it is very important to support diagnosticians and physiotherapists in recognizing self-care problems in children. OBJECTIVE: The aim of this paper is to extract classification and action rules, useful for those who work with children with disabilities. METHODS: First, features and their impact on the accuracy of classification are determined. Then, two models are built: one with all features and one with selected ones. For these models the classification rules are extracted. Finally, action rules are mined and the next step in treatment process is predicted. RESULTS: Seventeen features with the greatest impact on classifying a child into a particular group of self-care problems were identified. Based on the implemented algorithms, decision and action rules were obtained. CONCLUSIONS: The obtained model, selected attributes and extracted classification and action rules can support the work of therapists and direct their work to those areas of disability where even a minimal reduction of features would be of great benefit to the children.
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