Applications of Rule Based Classification Techniques for Thoracic Surgery (original) (raw)

Applying rule-based classification techniques to medical databases: an empirical study

2016

In the process of analysing and interpreting medical data, various classification techniques have been widely applied with a lot of success. A number of classification algorithms are available for this purpose and many researchers face the problem of choosing the best method for a particular dataset. In this paper, we apply five well-known, rule-based classification techniques on different medical datasets and compare their relative merits and demerits. Subsequently, we interpret their applicability in classifying patients into groups.

Application of Data Mining Techniques for Medical Data Classification: A Review

MATEC Web of Conferences

This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.

Clustering-based approach for medical data classification

Concurrency and Computation: Practice and Experience, 2018

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.

Role of Classification Algorithms in Medical domain: A Survey

In medical field, there are variety of diseases are available. In which breast cancer is the most common types of disease in women worldwide. Breast cancer is an uncontrolled growth of cell in the breast tissues. Tumor is abnormal cell growth that can be either benign or malignant. Benign tumors are noninvasive while malignant tumors are cancerous and spread to other parts of the body. Data Mining (DM) is the process of analyzing large quantities of data and summarizing it into useful information. In DM, number of classification algorithms used for medical data analysis. Some of such techniques involve analyzing breast cancer diagnosis. In this research, the classification algorithms C4.5, ID3, CART and C5.0 are compared with each other using medical data in general and breast cancer data in particular. Finally, among the analyzed algorithms, the best algorithm is identified from the different researcher's perspectives.

Data mining: medical and engineering case studies

In the paper the problem of predicting outcomes in medical and engineering applications is discussed. The problem is solved with a data mining approach. The first of the two algorithms presented in the paper generates a solution that is validated with the second algorithm. The validated solution constitutes the final decision.

A COMPARATIVE ANALYSIS OF CLASSIFICATION TECHNIQUES ON MEDICAL DATA SETS

Classification is the preliminary stage of data mining which is used to categorize the dataset in smaller groups where each group contains the similar data items. The classification basically deals with two main parameters; one is the number of classes and another is the criteria for deciding the class members. Different recognition algorithms also use the classification process as an initial stage to perk up the efficiency and the accuracy. The accuracy of the classification algorithm also decides the effectiveness of its use in other mining applications. The present work is about to analyze the effectiveness of the most popular classification techniques. In this paper, the analysis has been performed for five different classification algorithms in terms of accuracy, kappa statistics, execution time, mean absolute error under three datasets, collected from medical domain. The work has been implemented in WEKA environment and obtained results show that SVM is the most robust classification method and KNN is the least effective classifier for medical data sets.

DATA MINING TECHNIQUES IN MEDICAL SECTOR

Cutting edge open-source data mining suites of today have made some amazing progress from where they were just 10 years prior. New techniques are required attributable to the size and unpredictability of data accumulations in various dimensions like: organization, business and science. Today is an era of diseases where they are the major causes of death. The rising growth of medical problems has given popularity to use data mining techniques. Data mining has potentially improved the clinical decisions and survival time of patients[2]. Choosing the appropriate data mining technique is the main task because accuracy is the main issue. Earlier diagnosis done was based on doctor's experience or expertise but still wrong cases were reported. The objective is to give an exposure on variety of data mining techniques, so that the researchers can have direction to research on incurable diseases which are costliest diseases so as to save money and lives of the patient.

A Survey on Various Classification Techniques for Clinical Decision Support System

International Journal of Computer Applications, 2015

Clinical Decision Support systems link health observations with health knowledge to determine health option by clinicians for improved health care. The main idea of Clinical Decision Support System is a set of rules derived from medical professionals applied on a dynamic knowledge. Data mining is well suited to give decision support for the healthcare. There are several classification techniques available that can be used for clinical decision support system. Different techniques are used for different diagnosis. In this paper, various classification techniques for clinical decision support system are discussed with example. .

Data Mining Techniques for Medical Applications: A Survey

Data mining has been used to uncover hidden patterns and relations to summarize the data in ways to be useful and understandable in all types of businesses to make prediction for future perspective. Medical data is consider most famous application to mine that data, so in this paper we introduce a survey on how medical data problems such as dealing with noisy, incomplete , heterogeneous and intensive data has been faced ,the advantages and disadvantages of each one , finally suggest a framework for enhancing and overcoming this problems. The theory of fuzzy sets has been recognized as a suitable tool to model several kinds of patterns that can hold in data. In this paper, we are concerned with the development of a general model to discover association rules among items in a (crisp) set of fuzzy transactions. This general model can be particularized in several ways; each particular instance corresponds to a certain kind of pattern and/or repository of data. We describe some applications of this scheme, paying special attention to the discovery of fuzzy association rules .to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type .fuzzy association rules are developed as a sharp knife by handling quantitative data using fuzzy set. Along with the proposed system we will use neural network approaches for clustering, classification, statistical analysis and data modeling.