Disease Prediction using Enhanced Hybrid Algorithm with Manifold Dimensional Data
Dhivya S1, Anguraju K2, Suvitha K3, Preethi P4, Saravanabhavan C5
1Dhivya S, Assistant Professor, Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, Tami Nadu, India.
2Anguraju K, Assistant Professor, Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, Tami Nadu, India.
3Suvitha K, Assistant Professor, Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, Tami Nadu, India.
4Preethi P, Assistant Professor, Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, Tami Nadu, India.
5Saravanabhavan C, Head of the Department , Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, Tami Nadu, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 30, 2020. | PP: 353-359 | Volume-8 Issue-6, March 2020. | Retrieval Number: C6450098319/2020©BEIESP | DOI: 10.35940/ijrte.C6450.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Medicinal services industry has become huge business. The medicinal services industry creates a lot of social insurance information day by day that can be utilized to separate data for foreseeing malady that can happen to a patie future while utilizing the treatment history and wellbeing information This concealed data in the human services information will be later utilized for full of feeling basic leadership for patient’s wellbeing. Additionally, this region need improvement by utilizing the educational information social insurance. Significant test is the manner by which to extricate the data from these information on the grounds that the sum is enormous so a few information mining and machine learning systems can be utilized. Additionally, the normal result a-n extent of this venture is that in the event that malady can be anticipated, at that point lord y treatment can be given to the patients which can lessen the danger of life and spare existence of patient and cost t get treatment of ailments can be decreased up somewhat by duke acknowledgment. For this expert problem, a probabilistic displaying and profound learning approach will prepare a Long Short-Term Memory(LSTM) repetitive neural network and two convolutional neural systems for forecast of illness which is named as an upgraded half and half algorithm. The fast adoption of electronic wellbeing records has made an abundance of new information about patients, which is a goldmine for improving the understanding of human health. The above strategy is utilized to anticipate epidemic ailments utilizing quiet treatment history and wellbeing data. The parameter thought about is that this mode lessens test and train set blunder and increment information enlargement with better accuracy of 73.6%.
Keywords: Healthcare, LSTM, Epidemic, Inhabitants.
Scope of the Article: Healthcare Informatics