MARC details
| 000 -LEADER |
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| fixed length control field |
02499ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260101120155.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
260101t20252025my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010204 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .A89 2025 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mohd Syafiq Asyraf Bin Suhaimi, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Development of deep learning framework for prediction of cardiovascular disease / |
| Statement of responsibility, etc. |
Mohd Syafiq Asyraf Bin Suhaimi |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2025 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2025 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii, 111 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Centre for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang Al Sultan Abdullah– 2025 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Cardiovascular disease (CVD) remains the leading cause of death worldwide and our country, Malaysia. Hence, early detection is critical for effective intervention. This study aims to develop a deep learning framework for predicting CVD using data from smartwatches, which offer non-invasive and real-time health monitoring capabilities. The research employs two deep learning techniques: convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) networks. Electrocardiogram (ECG) and heart rate data were collected from 20 volunteers using local smartwatches supplemented with a publicly available dataset from Kaggle. Data pre-processing involved denoising ECG signals and normalizing heart rate readings to ensure accuracy. The models were evaluated using precision, recall, F1-score, and accuracy metrics, achieving over 99 percent across all measures. While both models demonstrated high predictive power, the LSTM model outperformed CNN in computational efficiency, with 31 minutes to complete model training compared to CNN, which took 87 minutes. The study highlights the potential for wearable devices for real-time CVD monitoring and early diagnosis. Future work will explore including additional data sources and advanced modeling involving ensemble techniques. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Centre for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |