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Standard Cog Exploration on Medicinal Data

by Vadamodula Prasad, Thamada Srinivasa Rao, Ankit Kumar Surana
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 10
Year of Publication: 2015
Authors: Vadamodula Prasad, Thamada Srinivasa Rao, Ankit Kumar Surana
10.5120/21106-3833

Vadamodula Prasad, Thamada Srinivasa Rao, Ankit Kumar Surana . Standard Cog Exploration on Medicinal Data. International Journal of Computer Applications. 119, 10 ( June 2015), 34-38. DOI=10.5120/21106-3833

@article{ 10.5120/21106-3833,
author = { Vadamodula Prasad, Thamada Srinivasa Rao, Ankit Kumar Surana },
title = { Standard Cog Exploration on Medicinal Data },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 10 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number10/21106-3833/ },
doi = { 10.5120/21106-3833 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:03:43.195884+05:30
%A Vadamodula Prasad
%A Thamada Srinivasa Rao
%A Ankit Kumar Surana
%T Standard Cog Exploration on Medicinal Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 10
%P 34-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we diagnose disease by implementing principal component analysis based on the user entered values for various symptoms. It considers components from the user entered values and compares them with the values in the database. Here the diagnosis is done by the method of prediction using trained data sets (TDS) and the results are compared by using suitable data matching systems (DMS). The TDS are provided by Intelligent System Laboratory of K. N. Toosi University of Technology, Imam Khomeini Hospital. If the user entered symptom values exist in the medical dataset then the percentage of getting an output that is true is 100%, if the symptom values do not exist, then the disease is predicted. PCA provides a precise disease prediction with the disease name and referral source which are optimized and neared to the true value in the datasets. Prediction based on machine learning algorithm gives an output that is 92%. This work predicts the actual levels of thyroid in human body.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Data matching system Database Prediction Principal component analysis Trained datasets.

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