K. Rajalakshmi ¹, K. Nirmala ²

Affiliations
1 Bharathiyar University, Coimbatore, Tamil Nadu, IN
2 Quaid-E-Millath Government College for Women, IN

Abstract

Objective: The industry of healthcare contains large information, which is difficult to process by manual methods. The large data are too valuable for extracting information and forming relationship from data mining area. Analysis: The experts and experienced doctors are not also available against the large population; sometimes symptoms are also being neglected. The existing system in the medical field is not able to extract all the information and knowledge from the heart disease database. Complex query for the healthcare practitioner to analyze the heart disease is still a challenging task. Finding: This paper is presenting a novel technique like K-Means, Weighted Associative Classifier (WAC) and Prediction Tree C5.0 for analyzing the heart disease and to sort out the existing issues. The K-Means is being used for unsupervised learning cluster within WAC. Initial centroid is being selected by the K-Means, which allow the classifier to extract the record and make a prediction for analyzing the disease with C5.0 prediction tree. The combined technology of K-Means, WAC and Prediction Tree C5.0 will provide a better, integrated, and accurate result over the heart disease prediction. The projected tool is MapReduce with Hive Database in Hadoop open source framework. Hadoop is perfectly compatible for big data projects. Improvements: The approaches for developing an Intelligent System for Heart Disease Prediction with big data mining will be very advantageous by automation of the proposed system K-Means, Prediction Tree C5.0, Weighted Association Classifier (WAC).

Keywords

Hive Database, K-Means Clustering, MapReduce, Prediction Tree C5.0, Weighted Association Classifier, Weighted Support and Confidence.

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M. Vijayalakshmi ¹, K. Rajalakshmi ¹

Affiliations
1 Department of Electrical and Electronics Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai – 600062, Tamil Nadu, IN

Abstract

Objectives: To increase the chances of saving the victims under debris especially those trapped under collapsed building. Methods: Technology used in this project is Internet of Things (IOT); through this technology the information about the victim trapped under debris is collected and stored in internet which is used for future use. A separate website is created to store the information about the workers in and out time and a GPS system is used to identify the victim location Findings: The simulation result explains about the latitude and longitude position of victim under rubble, Employer Id and the status of the victim under rubble. While comparing with the existing system the ARM processor is used where the analog to digital signal conversion is faster while comparing with PIC microcontroller, the information stored about the victim is useful for future purpose. Applications: Improvement is to reduce the size of the hardware and weight of the hardware. It is mainly applicable for saving the victims' life under collapsed building and under rubble.

Keywords

ARMLPC2148A, Collapsed Building, IOT Technology, Piezoelectric Plate and GPS, Victims.

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