International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 04 | Apr-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 840 Review on Tuberculosis Detection Using Various Data Mining Techniques Rupali Zakhmi 1 , Jyoti Arora 2 1 Research Scholar, Dept. Computer Science and Engineering, Desh Bhagat University, Punjab, India 2 Assistant Professor, Dept. Computer Science and Engineering, Desh Bhagat University, Punjab, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Data Mining is one of the most important and inspiring area of research with the goal of discovering purposeful information from massive data sets. Data mining plays an important role in healthcare field to detect causation of various diseases, their treatment methods .Tuberculosis is one of the well-known disorders among all the persons in the nation including India. Tuberculosis is a virus which strikes the immune system of an individual, usually transmits through air. It is primarily occurs in lungs. It is the typical cause of necrosis. This paper discussed about various data mining techniques to detect tuberculosis such as Classification, Clustering and Association. There are some parameters that are also useful to detect Tuberculosis like Age, Cough, Fever, Chest pain and Weight Loss etc. Key Words: Tuberculosis, Classification, Clustering, Association, Data Mining 1. INTRODUCTION Tuberculosis is a serious problem and transmits through bacteria known as Mycobacterium Tuberculosis. It is prime mover of demise. If right treatment is not given to the patient at proper time then it is very difficult to cure from TB. This disease is found on cattle, birds as well as in human being. The organs such as lungs are badly influenced by tuberculosis in most of the tuberculosis cases. TB attacks both grown-ups and kids. In early days, many different techniques were used such as sputum smear microscopy, chest radiography etc. These methods have several drawbacks, such as these methods require expertise to operate citified tools. These methods are suitable to obtain better results on time. Sometimes some symptoms of tuberculosis are same with other diseases, it leads to death. Incomplete information given by the patient or patient’s family can stand in the way to find right treatment. To control over these problems, some researcher use images, sounds or variables as inputs parameters. In this research, we will take some variables as input parameters to detect and identify tuberculosis. Most commonly data mining techniques have discussed in this research [2]. The aim of using data mining is to find significant information from vast data sets. It is also useful in the field of healthcare where unforeseen and relevant information are identified. Medical sector uses data mining techniques to know about various diseases, their causes and treatments. Techniques of data mining are very effective at the time of decision regarding patient heath. Medical data contains all about number of patients, cost of treatment, medical facilities etc. Analyzing this data, healthcare introduced powerful tool which extract important information that is necessary for patient’s recovery. It also verifies how much time is taken by patients for diagnosis. Identification of tuberculosis at right time is very important. To enhance the performance of patient’s treatment - Classification, Clustering and Association approaches have been introduced. Results of using data mining approaches provide benefits to healthcare domain by grouping the patients having same types of health issues [4]. Fig -1: Variables taken as input parameter to detect TB In Fig-1,Eight variables are used to identify and detect tuberculosis that are Age, Gender, Fever, Chest Pain, Weight Loss, Cough, Night sweats and Hemoglobin. Brain H. Tracey [8] uses cough detection algorithm to recover patient from pulmonary tuberculosis. Author proposed classification technique to decrease the cough count of patient.