CSEIT1723320 | Received : 12 June 2017 | Accepted : 21 June 2017 | May-June-2017 [(2)3: 872-875] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2017 IJSRCSEIT | Volume 2 | Issue 3 | ISSN : 2456-3307 872 A Classification Approach for instant Medical Assistance to Health Seekers Pritha Tikariha * , Prashant Richhariya Department of Computer Science, CSIT, Durg, Chhatishgarh, India ABSTRACT Data mining approach is applied in numerous numbers of fields for predicting and forecasting the events. However, in healthcare sectors, due to lack of faith in prediction method people hesitate to utilize data mining technique for health issues. People post their health related queries and get reply from the experts in many online healthcare applications. However, health seekers do not get instant assistance there; they need to wait for the experts for their opinion. Many data is accumulated in repository of such application. Using data mining techniques, useful information can be extracted from such repository, which can help health seekers to get instant assistance for their health related issues. These paper presents analysis on some data mining technique particularly in disease dataset. Three classification algorithms i.e. KNN, SVM, Naïve Bayes are applied on three disease dataset, to analyse the performance of the classifiers. The predictive rate is evaluated using four evaluation parameters i.e. Accuracy, precision, recall and f_measure. The experiment is performed in Matlab tool shows that Naive Bayes outperforms as compared to rest of the classifiers. Keywords : Arduino, Wi-Fi (ESP 8266), Load cell, Database System I. INTRODUCTION In health care domain, data can be very valuable. These data can be mined and converted into useful information. Medical data mining provides a way to explore the hidden relationship present in the data set of the medical realm. This relationship can be used for the diagnosis of many diseases. However, these medical data sets are very huge and obscure. These dataset have to be structure and assimilated to form a medical information system. Medical data mining provides a way to achieve these things. Patients generally have different medical attribute as a result they have heterogeneous medical requirement. These heterogeneous attribute needs to be classified accordingly and transformed into homogeneous groups. For converting it into homogeneous groups, these dataset require detailed, effective and efficient classification algorithms. Homogeneity brings the benefits of increased certainty in clinical diagnosis, predicting individual patient needs and resource utilization. In these research work three classification algorithms is applied on different disease dataset, to determine the prediction rate of the classifier. KNN, SVM and Naïve Bayes algorithm are analysed using for evaluation parameters accuracy, precision, recall and f-measure. The remaining paper is organized in the following way: Section II reviews the previous work done on different dataset and classifiers. Section III describes the methodology and the dataset. Section IV includes the experimental setup results and finally section V has conclusion of the research work and the future scope. II. LITERATURE SURVEY Classification algorithms are generally very useful for medicinal issues, especially when applied for the diagnosis purpose [4]. Many machine learning algorithms are applied in the medical domain in the course of recent decades [5] [6] [7] [8] [9] [10].A large portion of these applications are specific and include machine learning procedure like using data mining for diagnosis purpose [11], applied neural network rule for the prediction of breast cancer [10]. Data mining has