Data mining techniques for verification of medicine contents related to cardiac problems

DATA MINING TECHNIQUES FOR VERIFICATION OF
MEDICINE CONTENTS RELATED TO CARDIAC PROBLEMS
Shaikh Abdul Hannan 1, Pravin Yannawar2, Dr. R.R. Manza 2 , Dr. R. J. Ramteke 3,
1Vivekanand College, Aurangabad, India ([email protected])
2 Department of Computer Science and Information Technology, Dr. B.A.M.U., Aurangabad
3 Department of Computer Science and IT, Reader, NMU, Jalgaon ([email protected])

ABSTRACT:
symptoms you experience depend on the type and multidisciplinary areas, the knowledge discovery severity of your heart condition. Learn to recognize and data mining (KDD) field has made significant your symptoms and the situations that cause them. progress in the past decade. The field is vibrant Call your doctor if you begin to have new with significant impact on a wide variety of symptoms or if they become more frequent or science, business and technology areas where it has severe. The most common symptom is angina. become necessary to deal with exponentially Angina can be described as a discomfort, increasing volume of data. In this paper the heaviness, pressure, aching, burning, fullness, association rules as a means of identifying squeezing or painful feeling in your chest. It can be relationships among sets of symptoms, side effects and medicine, which can be used to evaluate trends Medicine Name
and classify groups. This concept is applied on medicine database to identify diseases on the basis of symptoms and by avoiding medicine side effects prepare prescription for patients. In this paper more than 100 patients’ data have been collected and output is verified by expert Doctor. As per Table. 1: Medicine ID, Medicine name and
Doctors opinion this output is nearby their outputs. Medicine side Effect
The outcome of the result shown here will be useful for Doctors to give appropriate medicine to Side Effect Description
patient on the basis of symptoms [1][2][3] KEYWORDS:
Introduction: The scalability of mining algorithms
has become a major research topic. One approach Table 2 : Side effect ID and its description
to the scalability problem is to run data mining Disease Name
algorithm on a small subset of the data. This strategy can yield useful approximate results in a fraction of the time required to compute the exact solution, thereby speeding up the mining process by orders of magnitude[4][5][6][7][8][9][10]. Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from Table 3 Disease ID, Disease Name and
different perspectives and summarizing it into Symptoms ID
useful information. Information that can be used to increase Symptom Description
both[11][12][13][14]. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize Table. 4 Symptoms ID and Symptoms
Description
Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. One of the reasons behind maintaining any database is to enable the user to find interesting patterns and trends in the Table. 5 Diseases ID and Medicine ID
Heart disease symptoms and medicine :
Experimental Analysis : In experiment samples
Coronary artery disease, heart attack -- each type of from 100 patients has been collected from Sahara heart disease has different symptoms, although Hospital, under the guidance of Dr. Abdul Jabbar many heart problems have similar symptoms The (MD Medicine). According to observation of samples the information has been arranged in Heart attack with chest pain
formats like, Medicine which consists of Medicine Medicine Name
Id, Medicine Name and Medicine Side effect as Iso sorbide dinitrate, Ramipril, nikorandil, shown in table 1. Where as table 2 shows the side trimetazidine, metoprolol, clopidogrel, atorvastatin, effect ID and description about side effect. The corresponding ID Number of side effect of table 2 Conclusion
has written in appropriate row of medicine ID and This work is based on association rules of Medicine name in table 1. Table 4 has given the data mining technique for verification of medicine information about symptoms description and its ID contents related to the cardiac problems. In the number. The Symptoms ID number has been listed experimental work it has been already considered out in the corresponding rows of disease ID and in all the cases of patients that the physical disease description in table 3. The last table 5 as examination of all the patients is found normal. In given the relationship among disease ID with the the result the information generated gives the suitable medicine by giving medicine ID number relation about disease with medicine, disease with along with disease ID number. In this way the symptoms, patient details with medicine. The final samples information has been arranged in various results have been verified by Dr. Abdul Jabbar tables. Each table consists of around 150 entries (MD Medicine). He is satisfactory about the work out of them few important has given in tables 1 to and all results. Further he has suggested that the work can extend in future by considering physical examination along with current inputs to generate patients information, symptoms of disease are good results. Therefore according to his taken as input by considering all physical suggestions this work in future may be extended to examination were normal. Based on given inputs the appropriate medicines were suggested as References:
prescription for patient in result. By applying set 1. Keon-Myung Lee, “Mining generalized fuzzy of association rules to avoid such medicine which are harmful to the patient the final prescription is generated as output. In the experimental work near Conference, IFSA World Congress and 20th about 100 side effects, 75 medicines, 10 different NAFIPS, Page(s):2977 - 2982 vol.5, 25-28 July diseases few symptoms has stored in data base. The results produced are in various forms like 2. De-Xing Wang; Xue-Gang Hu; Xiao-Ping Liu; individual patient, all patient, disease and medicine Hao Wang; Jun Guo, “Research on model of information, disease with symptoms report, specific association rules mining with added-newly measure disease wise symptom report, all patients, symptom and suggested medicine information, individual 3. Rastogi, R.; Kyuseok Shim, “Mining optimized information etc. One sample output of individual association rules with categorical and numeric patient which consists of patient name, age, symptoms, disease and list of suggested medicine. Like this more than 5 different formats as used to produce patient reports as output of this research 4. Yingjiu Li; Peng Ning; Wang, X.S.; Jajodia, S., work. All results are satisfactory and verified by “Discovering calendar-based temporal association Individual report of the patient :
Jamkar Gangadhar Keshawrao Age
Reasoning, Page(s):111 – 118, 14-16 June, 2001. 5. Xunwei Zhou; Hong Bao, “Mining Double- PE Y -->Y: YES, Physical Examination,
Connective Association Rules from Multiple Aurangabad found Normal (* PE : Physical Symptom Description
Engineering, Page(s):271 – 279, Volume 4, 12-14 List of Possible Disease
6. Ishibuchi, H.; Yamamoto, T.; Nakashima, T., “Determination of rule weights of fuzzy association List of Suggested Medicine
Page(s):1555 – 1558, Volume 3, 2-5 Dec. 2001. Trimetazidine, Ramipril, Iso sorbide dinitrate, Specific Disease and Medicine Information

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