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A COMPARATIVE ANALYSIS OF MULTIPLE APPROACHES MACHINE LEARNING FOR PREDICTING AND ANALYSING URINE PH AMOUNT

Fuente: Zenodo
Guardado en:
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Autores principales: Prajna Bhunia, Sirsendu Das Adhikary, Supriya Maity, Dipankar Dey, Samiram Pal
Formato: Recurso digital
Lenguaje:inglés
Publicado: Zenodo 2023
Materias:
Machine Learning
Linear Regression
Support Vector Machine
Neural Network
Gaussian Process Regression
Tree
Urine
pH
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_version_ 1866902004727545856
author Prajna Bhunia
Sirsendu Das Adhikary
Supriya Maity
Dipankar Dey
Samiram Pal
author_facet Prajna Bhunia
Sirsendu Das Adhikary
Supriya Maity
Dipankar Dey
Samiram Pal
contents <div> <h1><span>A comparative analysis of multiple approaches machine learning for predicting and analysing urine pH amount</span></h1> <h2><span><sup><span>a</span></sup></span><span><span>Prajna<span> </span>Bhunia</span></span><span><span> </span></span><span><span>,</span></span><span><span> </span></span><span><sup><span>b</span></sup></span><span><span>Sirsendu<span> </span>Das<span> </span>Adhikary,<span> </span></span></span><span><sup><span>c</span></sup></span><span><span>Supriya<span> </span>Maity,<span> </span></span></span><span><sup><span>d</span></sup></span><span><span>Dipankar<span> </span><span>Dey</span>,<span> </span></span></span><span><sup><span>e</span></sup></span><span><span>Samiram Pal</span></span></h2> <p><span><em><sup><span>abcde</span></sup></em></span><span><em><span>Global<span> </span>Institute<span> </span>of<span> </span>Science<span> </span>&<span> </span>Technology,<span> </span><span>Haldia</span>, <span>Purba</span><span> </span><span>Midnapur-721657,</span><span> </span><span>West</span><span> </span><span>Bengal,</span><span> </span><span>India</span></span></em></span></p> <p><span><em><span>Email:</span></em></span><span><em><span> </span></em></span><a href="mailto:sirsendu1979@gmail.com"><span><em><span>sirsendu1979@gmail.com,</span></em></span></a><span><em><span> </span></em></span><a href="mailto:supriyamaity1234@gmail.com"><span><em><span>supriyamaity1234@gmail.com,</span></em></span></a><span><em><span> </span></em></span><span><em><span>deydipankar2014@gmail.com, samiran.sip@gmail.com</span></em></span></p> <p><span><span> </span></span></p> </div> <p><strong><span>ABSTRACT</span></strong></p> <p><span>Prenatal<span> </span>treatment<span> </span>includes<span> </span>clinical<span> </span>urine<span> </span>testing<span> </span>as<span> </span>a<span> </span>crucial<span> </span>element.Medical<span> </span>professionals<span> </span>now evaluate<span> </span>urine<span> </span>test<span> </span>strips<span> </span>using<span> </span>an<span> </span>operator-dependent,<span> </span>labor-intensive,<span> </span>and<span> </span>visually<span> </span>color-coded<span> </span>process<span> </span>that takes<span> </span>a<span> </span>long<span> </span>time.<span> </span>Procedures<span> </span>and<span> </span>methods: By<span> </span>using<span> </span>various<span> </span>treatment<span> </span>and<span> </span>resource<span> </span>recovery<span> </span>techniques, urine<span> </span>has<span> </span>the<span> </span>potential<span> </span>to<span> </span>offer<span> </span>numerous<span> </span>useful<span> </span>resources. Selecting<span> </span>which<span> </span>technique<span> </span>to<span> </span>utilize<span> </span>and<span> </span>what<span> </span>re- sources<span> </span>might<span> </span>be<span> </span>retrieved<span> </span>from<span> </span>human<span> </span>urine,<span> </span>we<span> </span>paid<span> </span>particular<span> </span>attention<span> </span>to<span> </span>pH<span> </span>because<span> </span>it<span> </span>was<span> </span>thought<span> </span>to<span> </span>be the<span> </span>most<span> </span>significant<span> </span>parameter.<span> </span>We<span> </span>made<span> </span>a<span> </span>distinction<span> </span>between<span> </span>fresh,<span> </span>hydrolyzed,<span> </span>and<span> </span>stabilized<span> </span>urine<span> </span>treat- ment<span> </span>methods.<span> </span>For<span> </span>optimum<span> </span>resource<span> </span>recovery,<span> </span>future<span> </span>studies<span> </span>should<span> </span>concentrate<span> </span>on<span> </span>a<span> </span>thorough<span> </span>economic and life-cycle assessment of the urine treatment process.<span> </span>It has been shown that ML and AI are beneficial in a variety of fields, particularly with the current explosion of data.<span> </span>Making quicker and more accurate judgments<span> </span>in<span> </span>terms<span> </span>of<span> </span>illness<span> </span>forecasts<span> </span>may<span> </span>be<span> </span>possible<span> </span>using<span> </span>this<span> </span>method. Machine<span> </span>learning<span> </span>algorithms<span> </span>are therefore<span> </span>increasingly<span> </span>being<span> </span>used<span> </span>in<span> </span>prediction<span> </span>applications. Because<span> </span>of<span> </span>its<span> </span>high<span> </span>degree<span> </span>of<span> </span>accuracy,<span> </span>ML<span> </span>has been<span> </span>adopted<span> </span>by<span> </span>clinical<span> </span>diagnostics<span> </span>as<span> </span>one<span> </span>of<span> </span>the<span> </span>main<span> </span>computational<span> </span>approaches<span> </span>and<span> </span>analytics<span> </span>for<span> </span>illness identification.<span> </span>In order to increase the consistency and quality of disease reporting, building a model can also<span> </span>help<span> </span>us<span> </span>visualize<span> </span>and<span> </span>analyze<span> </span>diseases.<span> </span>This<span> </span>article<span> </span>has<span> </span>investigated<span> </span>how<span> </span>to<span> </span>predict<span> </span>the<span> </span>average<span> </span>pH<span> </span>value of<span> </span>urine.<span> </span>Different<span> </span>ML<span> </span>algorithms,<span> </span>including<span> </span>Linear<span> </span>Regression,<span> </span>Support<span> </span>Vector<span> </span>Machine,<span> </span>Neural<span> </span>Network, Gaussian<span> </span>Process<span> </span>Regression,<span> </span>and<span> </span>Fine<span> </span>Tree,<span> </span>are<span> </span>used<span> </span>to<span> </span>learn<span> </span>and<span> </span>find<span> </span>meaningful<span> </span>patterns. There<span> </span>are<span> </span>sev- eral<span> </span>insightful<span> </span>discoveries<span> </span>in<span> </span>this<span> </span>article.<span> </span>The<span> </span><em>R</em><sup>2</sup><span> </span>number<span> </span>is<span> </span>used<span> </span>to<span> </span>assess<span> </span>the<span> </span>accuracy<span> </span>of<span> </span>machine<span> learning </span>methods,<span> </span>including<span> </span>Fine<span> </span>Tree,<span> </span>Gaussian<span> </span>Process<span> </span>Regression,<span> </span>Neural<span> </span>Network,<span> </span>Support<span> </span>Vector<span> </span>Machine,<span> </span>and Linear Regression.<span> </span>According to recent research, the Linear Regression algorithm has the lowest RMSE value when compared to other algorithms and a high accuracy rate of 0<em>.</em>99997 for <em>R</em><sup>2</sup>.<span> </span>Nevertheless, the difficult<span> </span>and<span> </span>future<span> </span>research<span> </span>area<span> </span>for<span> </span>these<span> </span>studies<span> </span>will<span> </span>be<span> </span>to<span> </span>raise<span> </span>the<span> </span>accuracy<span> </span>rates<span> </span>of<span> </span>the<span> </span>machine<span> </span>learning <span>algorithms.</span></span></p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_14740345
institution Zenodo
language eng
publishDate 2023
publisher Zenodo
record_format zenodo
spellingShingle A COMPARATIVE ANALYSIS OF MULTIPLE APPROACHES MACHINE LEARNING FOR PREDICTING AND ANALYSING URINE PH AMOUNT
Prajna Bhunia
Sirsendu Das Adhikary
Supriya Maity
Dipankar Dey
Samiram Pal
Machine Learning
Linear Regression
Support Vector Machine
Neural Network
Gaussian Process Regression
Tree
Urine
pH
<div> <h1><span>A comparative analysis of multiple approaches machine learning for predicting and analysing urine pH amount</span></h1> <h2><span><sup><span>a</span></sup></span><span><span>Prajna<span> </span>Bhunia</span></span><span><span> </span></span><span><span>,</span></span><span><span> </span></span><span><sup><span>b</span></sup></span><span><span>Sirsendu<span> </span>Das<span> </span>Adhikary,<span> </span></span></span><span><sup><span>c</span></sup></span><span><span>Supriya<span> </span>Maity,<span> </span></span></span><span><sup><span>d</span></sup></span><span><span>Dipankar<span> </span><span>Dey</span>,<span> </span></span></span><span><sup><span>e</span></sup></span><span><span>Samiram Pal</span></span></h2> <p><span><em><sup><span>abcde</span></sup></em></span><span><em><span>Global<span> </span>Institute<span> </span>of<span> </span>Science<span> </span>&<span> </span>Technology,<span> </span><span>Haldia</span>, <span>Purba</span><span> </span><span>Midnapur-721657,</span><span> </span><span>West</span><span> </span><span>Bengal,</span><span> </span><span>India</span></span></em></span></p> <p><span><em><span>Email:</span></em></span><span><em><span> </span></em></span><a href="mailto:sirsendu1979@gmail.com"><span><em><span>sirsendu1979@gmail.com,</span></em></span></a><span><em><span> </span></em></span><a href="mailto:supriyamaity1234@gmail.com"><span><em><span>supriyamaity1234@gmail.com,</span></em></span></a><span><em><span> </span></em></span><span><em><span>deydipankar2014@gmail.com, samiran.sip@gmail.com</span></em></span></p> <p><span><span> </span></span></p> </div> <p><strong><span>ABSTRACT</span></strong></p> <p><span>Prenatal<span> </span>treatment<span> </span>includes<span> </span>clinical<span> </span>urine<span> </span>testing<span> </span>as<span> </span>a<span> </span>crucial<span> </span>element.Medical<span> </span>professionals<span> </span>now evaluate<span> </span>urine<span> </span>test<span> </span>strips<span> </span>using<span> </span>an<span> </span>operator-dependent,<span> </span>labor-intensive,<span> </span>and<span> </span>visually<span> </span>color-coded<span> </span>process<span> </span>that takes<span> </span>a<span> </span>long<span> </span>time.<span> </span>Procedures<span> </span>and<span> </span>methods: By<span> </span>using<span> </span>various<span> </span>treatment<span> </span>and<span> </span>resource<span> </span>recovery<span> </span>techniques, urine<span> </span>has<span> </span>the<span> </span>potential<span> </span>to<span> </span>offer<span> </span>numerous<span> </span>useful<span> </span>resources. Selecting<span> </span>which<span> </span>technique<span> </span>to<span> </span>utilize<span> </span>and<span> </span>what<span> </span>re- sources<span> </span>might<span> </span>be<span> </span>retrieved<span> </span>from<span> </span>human<span> </span>urine,<span> </span>we<span> </span>paid<span> </span>particular<span> </span>attention<span> </span>to<span> </span>pH<span> </span>because<span> </span>it<span> </span>was<span> </span>thought<span> </span>to<span> </span>be the<span> </span>most<span> </span>significant<span> </span>parameter.<span> </span>We<span> </span>made<span> </span>a<span> </span>distinction<span> </span>between<span> </span>fresh,<span> </span>hydrolyzed,<span> </span>and<span> </span>stabilized<span> </span>urine<span> </span>treat- ment<span> </span>methods.<span> </span>For<span> </span>optimum<span> </span>resource<span> </span>recovery,<span> </span>future<span> </span>studies<span> </span>should<span> </span>concentrate<span> </span>on<span> </span>a<span> </span>thorough<span> </span>economic and life-cycle assessment of the urine treatment process.<span> </span>It has been shown that ML and AI are beneficial in a variety of fields, particularly with the current explosion of data.<span> </span>Making quicker and more accurate judgments<span> </span>in<span> </span>terms<span> </span>of<span> </span>illness<span> </span>forecasts<span> </span>may<span> </span>be<span> </span>possible<span> </span>using<span> </span>this<span> </span>method. Machine<span> </span>learning<span> </span>algorithms<span> </span>are therefore<span> </span>increasingly<span> </span>being<span> </span>used<span> </span>in<span> </span>prediction<span> </span>applications. Because<span> </span>of<span> </span>its<span> </span>high<span> </span>degree<span> </span>of<span> </span>accuracy,<span> </span>ML<span> </span>has been<span> </span>adopted<span> </span>by<span> </span>clinical<span> </span>diagnostics<span> </span>as<span> </span>one<span> </span>of<span> </span>the<span> </span>main<span> </span>computational<span> </span>approaches<span> </span>and<span> </span>analytics<span> </span>for<span> </span>illness identification.<span> </span>In order to increase the consistency and quality of disease reporting, building a model can also<span> </span>help<span> </span>us<span> </span>visualize<span> </span>and<span> </span>analyze<span> </span>diseases.<span> </span>This<span> </span>article<span> </span>has<span> </span>investigated<span> </span>how<span> </span>to<span> </span>predict<span> </span>the<span> </span>average<span> </span>pH<span> </span>value of<span> </span>urine.<span> </span>Different<span> </span>ML<span> </span>algorithms,<span> </span>including<span> </span>Linear<span> </span>Regression,<span> </span>Support<span> </span>Vector<span> </span>Machine,<span> </span>Neural<span> </span>Network, Gaussian<span> </span>Process<span> </span>Regression,<span> </span>and<span> </span>Fine<span> </span>Tree,<span> </span>are<span> </span>used<span> </span>to<span> </span>learn<span> </span>and<span> </span>find<span> </span>meaningful<span> </span>patterns. There<span> </span>are<span> </span>sev- eral<span> </span>insightful<span> </span>discoveries<span> </span>in<span> </span>this<span> </span>article.<span> </span>The<span> </span><em>R</em><sup>2</sup><span> </span>number<span> </span>is<span> </span>used<span> </span>to<span> </span>assess<span> </span>the<span> </span>accuracy<span> </span>of<span> </span>machine<span> learning </span>methods,<span> </span>including<span> </span>Fine<span> </span>Tree,<span> </span>Gaussian<span> </span>Process<span> </span>Regression,<span> </span>Neural<span> </span>Network,<span> </span>Support<span> </span>Vector<span> </span>Machine,<span> </span>and Linear Regression.<span> </span>According to recent research, the Linear Regression algorithm has the lowest RMSE value when compared to other algorithms and a high accuracy rate of 0<em>.</em>99997 for <em>R</em><sup>2</sup>.<span> </span>Nevertheless, the difficult<span> </span>and<span> </span>future<span> </span>research<span> </span>area<span> </span>for<span> </span>these<span> </span>studies<span> </span>will<span> </span>be<span> </span>to<span> </span>raise<span> </span>the<span> </span>accuracy<span> </span>rates<span> </span>of<span> </span>the<span> </span>machine<span> </span>learning <span>algorithms.</span></span></p>
title A COMPARATIVE ANALYSIS OF MULTIPLE APPROACHES MACHINE LEARNING FOR PREDICTING AND ANALYSING URINE PH AMOUNT
topic Machine Learning
Linear Regression
Support Vector Machine
Neural Network
Gaussian Process Regression
Tree
Urine
pH
url https://doi.org/10.5281/zenodo.14740345

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