Volume 6 Issue 12- December 2016
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401-405 | Segmented File Transfer Lalit Adithya, Sampada K S Abstract
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406-409 | Comparative Study of Packet Sniffing tools for
HTTP Network Monitoring and Analyzing Dr. Aruna Varanasi, P. Swathi Abstract
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410-413 | Automatic Safety Home Bell System with
Message Enabled Features T.Venkat Narayana Rao, Karttik Reddy Yellu Abstract
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414-418 | Internet of Things: Security Challenges and Issues T. Venkat Narayana Rao, Sangam Shayideep, Jayesh Jahagirdar Abstract
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419-422 | Estimation of Diabetes Employing Classifiers
and Machine Learning Methods Thota Aditya Kumar Abstract
Diabetes is considered to be as never-ending infection and deadliest disease which results in increase of glucose levels in the body. If it is not treated properly then it may cause many difficulties. The general approach of treating this disease is visiting of a patient to a specialist, but the result may not be accurate. But this problem will be solved by machine learning approaches. The objective of this study is to design a system that can predict the diabetes in the human body with maximum precision. Therefore, Machine learning classification algorithms namely SVM, KNN, Naïve Bayes and Logistic Regression are used in this experiment to detect diabetes at the starting stage. These experimentations are performed on Pima Indians Diabetes Database (PDID) which is drawn from UCI repository. The outcomes of the four algorithms are evaluated on diverse parameters such as Precision, Accuracy F-Measure, and Recall.
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