Data Mining applied to interventional cardiology procedures (original) (raw)
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Data Mining in Healthcare for Heart Diseases
Data Mining is the area of research which means digging of useful information or knowledge from previous data. There are different techniques used for the data mining. Data mining may used in different fields including Healthcare. Heart or Cardiovascular diseases are the very hot issue in Healthcare industry globally. Many patients died due to insufficient amount of knowledge. As Healthcare industry produces a huge amount of data, we may use data mining to find hidden patterns and interesting knowledge that may help in effective and efficient decision making. Data mining in Healthcare is a crucial and complicated task that needs to be executed accurately. It attempts to solve real world health problems in diagnosis and treatment of diseases. This work is also an attempt to find out interesting patterns from data of heart patients. There are three algorithm used with two different scenarios. These implemented algorithms are Decision Tree, Neural Network and Naïve Bayes.
Cardiac data mining (CDM); organization and predictive analytics on biomedical (cardiac) data
2013
Data mining and data analytics has been of immense importance to many different fields as we witness the evolution of data sciences over recent years. Biostatistics and Medical Informatics has proved to be the foundation of many modern biological theories and analysis techniques. These are the fields which applies data mining practices along with statistical models to discover hidden trends from data that comprises of biological experiments or procedures on different entities. The objective of this research study is to develop a system for the efficient extraction, transformation and loading of such data from cardiologic procedure reports given by Armed Forces Institute of Cardiology. It also aims to devise a model for the predictive analysis and classification of this data to some important classes as required by cardiologists all around the world. This includes predicting patient impressions and other important features.
Prediction of Heart Disease by Clustering and Classification Techniques
International Journal of Computer Sciences and Engineering, 2019
Every year 19 million people approximately die from heart disease worldwide. A heart patient shows several symptoms and it is very tough to attribute them to the heart disease in so many steps of disease progression. Data mining, as an answer to extract a hidden pattern from the clinical dataset, are applied to a database in this analysis. All available algorithms in classification technique are compared to each other to achieve the highest accuracy. To further increase the correctness of the solution, the dataset is preprocessed by different unsupervised and supervised algorithms. The two important tasks which are needed for the development of classifier come under data mining and they are clustering and classification. In K-means clustering the initial point selection effects on the results of the algorithm, both in the number of clusters found and their centroids. Methods to enhance the k-means clustering algorithm are discussed. With the help of these methods efficiency, accuracy and performance are improved. So, to improve the performance of clusters the Normalization which is a preprocessing stage is used to enhance the Euclidean distance by calculating more nearer centers, which result in a reduced number of iterations which will reduce the computational time as compared to k-means clustering. Finally, the classifiers are developed with Logistic regression by using the data extracted by K-Means Clustering. The techniques adopted in the design of classifier perform relatively well in terms of classification results better compared to clustering techniques.
USING DATA MINING TECHNIQUES IN HEART DISEASE DIAGNOSIS AND TREATMENT
Abstract— The availability of huge amounts of medical data leads to the need for powerful data analysis tools to extract useful knowledge. Researchers have long been concerned with applying statistical and data mining tools to improve data analysis on large data sets. Disease diagnosis is one of the applications where data mining tools are proving successful results. Heart disease is the leading cause of death all over the world in the past ten years. Several researchers are using statistical and data mining tools to help health care professionals in the diagnosis of heart disease. Using single data mining technique in the diagnosis of heart disease has been comprehensively investigated showing acceptable levels of accuracy. Recently, researchers have been investigating the effect of hybridizing more than one technique showing enhanced results in the diagnosis of heart disease. However, using data mining techniques to identify a suitable treatment for heart disease patients has received less attention. This paper identifies gaps in the research on heart disease diagnosis and treatment and proposes a model to systematically close those gaps to discover if applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease. Keywords- E-health, Data Mining, Heart Disease Diagnosis and Treatment
2013
Medical errors are both costly and harmful. Medical errors cause thousands of deaths worldwide each year. A clinical decision support system (CDSS) offers opportunities to reduce medical errors as well as to improve patient safety. One of the most important applications of such systems is in diagnosis and treatment of heart diseases (HD) because statistics have shown that heart disease is one of the leading causes of deaths all over the world. Data mining techniques have been very effective in designing clinical support systems because of its ability discover hidden patterns and relationships in medical data. This paper compares the performance and working of six CDSS systems which use different data mining techniques for heart disease prediction and diagnosis. This paper also finds out that there is no system to identify treatment options for HD patients.
Diagnosis Analysis of Cardiovascular Disease Using With Data Mining Techniques
In this paper, we discuss diagnosis analysis and identification of cardiovascular disease using with data mining techniques. The cardiovascular disease is a major cause of morbidity and mortality in modern society; it is extremely important but complicated task that should be performed accurately and efficiently. It is an huge amount data of leads medical data to the need for powerful data analysis tools are availability on the data mining technique. They have long to been an concerned with applying for statistical and data mining tools and data mining techniques to improve data analysis on large datastes.In this paper, to proposed system are implemented to find out the cardiovascular disease through as to compared with the some data mining techniques are Decision tree, SOM, CN2 Rule and K-Means Clustering the data mining could help in the identification or the prediction of high or low risk of Cardiovascular Disease.
Integrating Clustering with Different Data Mining Techniques in the Diagnosis of Heart Disease
Heart disease is the leading cause of death in the world over the past 10 years. The research presented here is part of work to develop tools to assist healthcare practitioners to diagnosis heart disease earlier in the hope of earlier interventions in this preventable killer. The relative accuracy of common data mining techniques in heart disease diagnosis is difficult to assess from the literature. This research investigates Decision Tree, Naïve Bayes, and K-nearest Neighbour performance in the diagnosis of heart disease patients. It then further assesses performance enhancement through integrating clustering techniques. The testing was conducted over a standardized dataset used widely in the literature. The results show that integrating clustering with decision tree, naïve bayes, and k nearest neighbour could enhance their accuracies in diagnosing heart disease patients. Importantly, the results establish that the ensemble of two cluster inlier k-means clustering with the knearest neighbour technique was the most effective at heart disease diagnosis.
Cardiovascular Disease Analysis Using Supervised and Unsupervised Data Mining Techniques
Cardiovascular diseases are the main cause of death around the world. Every year, more people die from these diseases than from any other cause. According to World Health Organization data, in 2012 more than 17,5 million people died from this cause, and that represents 31% of all deaths registered worldwide. Data mining techniques are widely used for the analysis of diseases, including cardiovascular conditions, and the techniques used in the proposed method in this research are decision trees, support vector machines, bayesian networks and k-nearest neighbors. Apart from the previous techniques, it was necessary to use a clustering method for data segmentation according to their diagnosis. As a result, the Simple K-Means clustering method and the support vector machines technique obtained the best levels in metrics such as precision (97%), coverage (97%), true positive rate (97%) and false positive rate (0.02%), and this can be taken as evidence that the proposed method can be used assertively as decision making support to diagnose a patient with cardiovascular disease.
Novel Approach for Heart Disease using Data Mining Techniques
International Journal of Advance Research, Ideas and Innovations in Technology, 2016
Data mining is the process of analysing large sets of data and then extracting the meaning of the data. It helps in predicting future trends and patterns, allowing business in decision making. Presently various algorithms are available for clustering the proposed data, in the existing work they used K mean clustering, C4.5 algorithm and MAFIA i.e. Maximal Frequent Item set algorithm for Heart disease prediction system and achieved the accuracy of 89%. As we can see that there is vast scope of improvement in our proposed system, in this paper we will implement various other algorithms for clustering and classifying data and will achieved the accuracy more than the present algorithm. Several Parameters has been proposed for heart disease prediction system but there have been always a need for better parameters or algorithms to improve the performance of heart disease prediction system.