Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021.
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| Idioma: | English |
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Brac University
2021
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| Acesso em linha: | http://hdl.handle.net/10361/15613 |
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10361-156132021-11-15T21:01:34Z Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module Shrestha, Soptorsi Paul Amin, Md. Hasnatul Faisal, MD. Amir Alam, Syed Md. Jawadul Azad, A. K. M. Abdul Malek Department of Electrical and Electronic Engineering, Brac University Myocardial infarction GSM GPS Text message Standard scalar method K-Nearest Neighbor Random forest Support Vector Machine Naive bayes ECG Mortality rate Myocardial infarction Medicine--Research Global system for mobile communications This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 81-88). Myocardial Infarction (MI) is a crucially leading reasons of huge mortality and modality all over the world. The prior reasons for most of the demise are delayed diagnosis and disrupted medical attention. Our endeavor objectifies developing a portable system to detect MI instantly and incorporating a forewarning system using GSM and GPS module. The paper is focusing on the warning system delivering text messages containing classified MI information. Initially, the dataset has been preprocessed using Standard Scalar method and the preprocessed data has been trained and tested using K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM) and Naive Bayes (NB) to distinguish the MI affected ECG from normal ECG signal. The aim of this project is to avail immediate attention to a MI affected patient to ensure medical deliberation rapidly. Proper activation of the system will minimize the deadly effect of MI and hence reduce the mortality rate due to MI. Soptorsi Paul Shrestha Md. Hasnatul Amin MD. Amir Faisal Syed Md. Jawadul Alam B. Electrical and Electronic Engineering 2021-11-15T06:11:53Z 2021-11-15T06:11:53Z 2021 2021-09 Thesis ID 17221001 ID 18121107 ID 17221008 ID 18121064 http://hdl.handle.net/10361/15613 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 126 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Myocardial infarction GSM GPS Text message Standard scalar method K-Nearest Neighbor Random forest Support Vector Machine Naive bayes ECG Mortality rate Myocardial infarction Medicine--Research Global system for mobile communications |
| spellingShingle |
Myocardial infarction GSM GPS Text message Standard scalar method K-Nearest Neighbor Random forest Support Vector Machine Naive bayes ECG Mortality rate Myocardial infarction Medicine--Research Global system for mobile communications Shrestha, Soptorsi Paul Amin, Md. Hasnatul Faisal, MD. Amir Alam, Syed Md. Jawadul Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2021. |
| author2 |
Azad, A. K. M. Abdul Malek |
| author_facet |
Azad, A. K. M. Abdul Malek Shrestha, Soptorsi Paul Amin, Md. Hasnatul Faisal, MD. Amir Alam, Syed Md. Jawadul |
| format |
Thesis |
| author |
Shrestha, Soptorsi Paul Amin, Md. Hasnatul Faisal, MD. Amir Alam, Syed Md. Jawadul |
| author_sort |
Shrestha, Soptorsi Paul |
| title |
Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module |
| title_short |
Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module |
| title_full |
Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module |
| title_fullStr |
Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module |
| title_full_unstemmed |
Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module |
| title_sort |
automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using gsm module |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15613 |
| work_keys_str_mv |
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| _version_ |
1814309287338442752 |