Lehality Prediction of Highly Disproportionate Data of ICU Deceased using Extreme Learning Machine
A. Vidya1, D. Shanthi2, P. Gokulakrishnan3, K. Manivannan4
1A. Vidya, CSE Department, PSNACET, Dindigul, Tamilnadu , India.
2D. Shanthi, Professor & Head , CSE Department, PSNACET, Dindigul, Tamilnadu , India.
3P. Gokulakrishnan, Associate Professor , CSE Department, PSNACET, Dindigul, Tamilnadu , India.
4K. Manivannan, Associate Professor , CSE Department, PSNACET, Dindigul, Tamilnadu , India
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 47-51 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I11490789S219/19©BEIESP DOI: 10.35940/ijitee.I1149.0789S219
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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: Big data in mortality prediction is rationed with enormous amount of dataset of patients admitted in ICU for the healthcare providers to clarify and interpret about the status of the patients. However, it is difficult to process these large datasets for which big data is used. Mortality prediction of patients admitted in ICU faces many challenges such as imbalance distribution, high dimensionality etc. This paper focuses on overcoming the challenges that arise during the prediction of mortality of ICU patients through pre-processing, feature selection, feature extraction, and classification have been developed. The performance of classifiers has been affected by the high dimensional and unbalanced data of patients. Therefore, a classifier called Extreme Learning Machine has been used for a generalized performance of the classification. In order to predict the rate of mortality for the patients admitted in the ICU by solving the challenges using various methods and tools. For this work, the dataset is collected from a rural hospital that provides medical services in the particular locality. To evaluate the performance of the proposed model, various algorithms have been used and the obtained results are compared. The proposed approach is implemented and experimented in MATLAB software. Various statistical reports are obtained as results and verified. From the results and comparison, it is noticed that the proposed method outperforms than other approaches.
Keywords: Mortality Prediction, Extreme Learning Machine.
Scope of the Article: Smart Learning Methods and Environments