Artificial plant optimization algorithm to detect heart rate & presence of heart disease using machine learning.
In today's world, cardiovascular diseases are prevalent becoming the leading cause of death; more than half of the cardiovascular diseases are due to Coronary Heart Disease (CHD) which generates the demand of predicting them timely so that people can take precautions or treatment before it becomes f...
| Publicado en: | Artificial Intelligence in Medicine Vol. 102 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Jan2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141319880&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141319880 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Jan2020 vid: 102 pid: 1004 pub: Elsevier B.V. artinfo: ui: 141319880 141319880 NLM31980091 10.1016/j.artmed.2019.101752 NLM31980091 141319880 ppct: 1 formats: tig: atl: Artificial plant optimization algorithm to detect heart rate & presence of heart disease using machine learning. aug: au: Sharma, Prerna Choudhary, Krishna Gupta, Kshitij Chawla, Rahul Gupta, Deepak Sharma, Arun affil: Maharaja Agrasen Institute of Technology, Delhi, India sug: subj: Heart Rate Artificial Intelligence Heart Diseases Physiopathology Plants Predictive Value of Tests Programming Languages Algorithms Energy Metabolism Plethysmography Coronary Disease Physiopathology Resource Databases ab: In today's world, cardiovascular diseases are prevalent becoming the leading cause of death; more than half of the cardiovascular diseases are due to Coronary Heart Disease (CHD) which generates the demand of predicting them timely so that people can take precautions or treatment before it becomes fatal. For serving this purpose a Modified Artificial Plant Optimization (MAPO) algorithm has been proposed which can be used as an optimal feature selector along with other machine learning algorithms to predict the heart rate using the fingertip video dataset which further predicts the presence or absence of Coronary Heart Disease in an individual at the moment. Initially, the video dataset has been pre-processed, noise is filtered and then MAPO is applied to predict the heart rate with a Pearson correlation and Standard Error Estimate of 0.9541 and 2.418 respectively. The predicted heart rate is used as a feature in other two datasets and MAPO is again applied to optimize the features of both datasets. Different machine learning algorithms are then applied to the optimized dataset to predict values for presence of current heart disease. The result shows that MAPO reduces the dimensionality to the most significant information with comparable accuracies for different machine learning models with maximum dimensionality reduction of 81.25%. MAPO has been compared with other optimizers and outperforms them with better accuracy. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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