MARC details
| 000 -LEADER |
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03213ntm a2200277 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000099137 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251117113334.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
170419t2016 my a f am 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0001112(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201905141152 |
| Level of effort used to assign nonsubject heading access points |
nazirah |
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201704191028 |
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saini |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FSKKP .F38 2016 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Fatin Athirah Nasoha |
| 245 10 - TITLE STATEMENT |
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| Title |
Prediction of chronic kidney disease using artificial neural network / |
| Statement of responsibility, etc. |
Fatin Athirah Nasoha |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2016 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xvi, 56 p. : |
| Other physical details |
ill. (some col.) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computer Systems and Software Engineering |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Project paper (Bachelor of Computer Science (Software Engineering) With Honours) -- Universiti Malaysia Pahang – 2016 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Bibliography : p. 54-55 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
This research paper is about Prediction of Chronic Kidney Disease Using Artificial Neural Network. As known, a pair of kidneys is one of the most important organs in the body to keep the composition or make-up of the blood stable which lets body function. Then, where the kidneys are damaged, it can possibly affect human body since the waste and extra fluid can build up themselves inside of the human body. Currently, the physician in a hospital or clinic will make a deep review and analyze of the complexity of patient data which many attributes. So, it takes longer time to make a prediction. Normally, physicians are predicting CKD by knowledge and experience disease and it is a difficult task in medical environment. So, sometimes it can cause the human error. The overall objectives in this research; to study the neural network method for prediction of Chronic Kidney Disease (CKD), to implement neural network prediction using dataset for reduce human error and to evaluate the result of prediction CKD. The methodology used for this research is Artificial Neural Network (ANN) by applying back-propagation algorithm and sigmoid function. Data normalization is performed such that the range is from 0 to 1 which is a max-min normalization or also known as sigmoid normalization. The neural network designed with 24-4-1, 24-6-1 and 24-8-1 model. In this research, it trains the dataset of kidney disease factor and test process for dataset to see the actual result. Then, test the dataset and verify the accuracy result of kidney disease condition with calculate the Mean Square Error (MSE) value to calculate the error function. Besides, Root Mean Square Error (RMSE) will be used as data fitting method. From this study, 24-6-1 model for prediction of chronic kidney disease by using artificial neural network validates the best RMSE value; within 0.035 to 0.04 respectively. However, from the test dataset, 24-8-1 model is finally chosen as the RMSE value is within 0.09 to 0.12 respectively. The reason is because the result shows more accurate when test a new data. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computer Systems and Software Engineering |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and Colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Theses |