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Data Analysis Classifier Model for Prediction of Human Ailments Using Artificial Neural Network

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Abstract
Recent trends has shown that health care delivered in industrialized nations often falls short of optimal, evidence based care. Clinical data are an ever growing source of information that is generated from the hospitals in the form of patient records, when these data are mined properly, the information hidden in these data are huge resource bank for medical research. These data often contain hidden patterns and relationships, which can lead to better diagnosis, better medicines, better treatment, and overall, a platform to better understand the mechanisms governing almost all aspects of the medical domain. Classification is a machine learning technique used to solve various problems like pattern classification, image processing, etc. Neural network is the effective tool for pattern classification. Classification of any data is important to know that the data are belongs to which group. With tremendous growing population, the doctors and experts available are not in proportion with the population. Hence it focuses on computing the probability of occurrence of a particular ailment from the medical data.
I.Introduction
Given the rapidly growing population, the increased burden of chronic diseases and the increasing health care costs, there is an urgent need for the development, implementation, and deployment, in every day medical practice, of new models of health care services. In this scenario, home monitoring and data mining play an important role. Data mining is the computer assisted process of digging through and analyzing a large quantity of data in order to extract meaningful knowledge and to identify phenomena faster and better than human experts. Data Mining, also popularly known as Knowledge. Discovery in Databases (KDD), refers to the nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases. Data mining is widely used in business (insurance, banking, retail), science research (astronomy, medicine), and government security (detection of criminals and terrorists). Other similar terms referring to data mining are data dredging, knowledge extraction and pattern discovery to address the deficiencies in care, health care organizations are increasingly turning to clinical decision support systems, which provide clinicians with patient specific assessments or recommendations to aid clinical decision making. In recent trends, the statistics reveals that technology always stayed back when it came to diagnosis, a process that still requires a doctor’s knowledge and their experience to process the sheer number of variables involved, ranging from medical history to climatic conditions, temperature, environment and various other factors. Since the number of variables are greater than the total number of variables which no model has successfully analyzed yet. To overcome this problem, medical decision support system are becoming more and more important, which will assist doctors as well as medical students in taking correct decisions. Classification is a data mining technique used to group item based on some key characteristic. It is used to classify each item in a set of data into one of predefined set of classes or groups. It can deals with the large amount of data that are involved in processing. They are being used in different industry to easily identify the type and group to which a particular tuple belongs. Classification approaches normally use a training set where all objects are already associated with known class labels. The classification algorithm learns from the training set and builds a model. The model is used to classify new objects. There are many algorithms which are used for classification in data mining are following: 1. Decision tree induction 2. Nearest neighbor classifier 3. Artificial Neural Network

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