Today the heart disease is one of the most important causes of death in the world. So its early prediction and diagnosis is important in medical field, which could help in on time treatment, decreasing health costs and decreasing death caused by it. In fact the main goal of using data mining algorithms in medicine by using patients’ data is better utilising the database and discovering tacit knowledge to help doctors in better decision making.
Therefore using data mining and discovering knowledge in cardiovascular centres could create a valuable knowledge, which improves the quality of service provided by managers, and could be used by doctors to predict the future behaviour of heart diseases using past records. Also some of the most important applications of data mining and knowledge discovery in heart patients system includes: diagnosing heart attack from various signs and properties, evaluating the risk factors which increases the heart attack.
In this article the effort focused on evaluating the previous works on discovering knowledge using data mining in heart diseases field, and also explain the used algorithms in every one of the previous works, to help the future researchers to gain maximum benefits from these abilities. Because of this, in the next sections, first we will explain various works in data mining field using heart patients’ data, and will show the ability of data mining in various applications of heart disease field, and based on a table will show the history of data mining and it’s applications in heart diseases field. Finally we will provide the best methods and algorithms used in various applications of heart diseases using a comparison and will show the results in a table. It is obvious in the diagrams that the suggested method has the best performance and best quality in prediction.
Daily increasing development in information technology caused in significant growth in sciences. One of the sciences is medical science. Using artificial intelligence techniques in all subjects of this branch of science especially cardiovascular diseases made it possible to design medical assistant systems. By taking attention to increase in new diseases and also extension of technologies, the diagnosis of diseases gone beyond the internal treatment style, and the most efforts of doctors and specialists is focused on early prediction of diseases using available signs. Medical information retrieval system is the best system for managing clinical data. This system is capable to healthcare operations in diagnosing diseases and has an important role in clinical decision making.
Cardiovascular diseases is one of the most spreading causes of death in worldwide. One main type of this disease is “coronary artery disease” (CAD), which about 25% of population without any previous signs, are suddenly subject of this disease, and experience severe heart attack and die. At the moment, Angiography uses for determining the amount and location of narrowing of the arteries of the heart, which has high price and several side effects. Using data mining for diagnosis of heart diseases may be very lower in price and very faster.
Based on the announced statistics by the World Health Organisation in 2005, there was 17.5 million victims from cardiovascular diseases, which is 30% of all death in worldwide, and it was predicted that this value increase to 23 million people up to 2030. Examinations made in Iran showed that 38% of all death subjects caused by cardiovascular diseases, which is increasing in future. Based on the statistics obtained from the evaluation of cardiovascular diseases, it was shown that 16.1% of people have high blood pressure, 43.9% have extra weight, 38.9% have low physical activity, and also 10.8% use tobacco which is of the most important factors of heart diseases.
Diagnosis of heart diseases is a significant and boring task and also an important duty in medical science, which requires extreme attention. However there is some tools for data extraction and analysis. Also existence of huge set of medical data leads to correct diagnosis of disease. Using medical data including age, sex, blood pressure, and blood sugar, it is possible to increase the possibility of heart diseases prediction. These data must be collected in organised manner, which could be used for integrating the prevention system.
In different countries using artificial intelligence techniques and various algorithms, predicting this type of death -heart disease- is somewhat possible. In Iran many efforts in this subject made by cooperation of software and medical communities. So the current study focuses on field studies by ail of decreasing the cost and early prediction of events happened for heart patients in Iran.
One of important methods in this field is clustering. In clustering, the data splits to some clusters, in such a way that the data in every cluster have maximum similarity with each other and minimum similarity with data of other clusters. So using clustering data will show that every cluster that has the patient, could help us in predicting that if he/she is under heart attack risk or not. Using this method and reaching to more precise diagnosis for heart patients is our aim.
We know that traditional clustering methods like K-Means often judges on data using the distance between them. But in this study the highest objection is using this property, because available data about heart patients includes binary and nominal data. So using various improved clustering methods, we can use other metrics instead of focusing on distance between data, to focus on qualitative properties, to increase the precision and gain more correct diagnosis. By comparing and clustering the techniques and algorithms which used in heart diseases field and its diagnosis, we will see that there is no algorithm which always has maximum performance, and various factors are effective in performance of algorithms.
Mechatronics Engineering Department, Computer and Electrical Engineering Faculty, Hakim Sabzevari University, Sabzevar, Iran
Associate Professor of Medical Engineering Department, Computer and Electrical Engineering Faculty, Hakim Sabzevari University, Sabzevar, Iran
Correspondence: Abbas Nasrabadi, Mechatronics Engineering Department, Computer and Electrical Engineering Faculty, Hakim Sabzevari University, Sabzevar, Iran.
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