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Classification of heart disease using backpropagation neural network / Nurul Atikah Mahadi

By:

Nurul Atikah Mahadi

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2016Description: xi, 55 p. : ill. (some col.) ; 30 cm. + 1 CD-ROMISBN:

THE0001086(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Computer Science (Computer Systems & Networking)) With Honours) -- Universiti Malaysia Pahang – 2016 Abstract: Normally, a heart disease patient is diagnosed with the disease at the hospital. Nowadays, there is no system available yet to diagnose a patient with heart disease in a fast pace. It is becoming necessary and crucial for human kind nowadays especially in Malaysia to have a solution for a faster diagnosis of heart disease. When a patient is diagnosed at the hospital, it is a time consuming as the complexity of data that has many attributes and doctor or physician is the only professional people that need to analyse the data. Besides, the high chances of fault diagnose may occur as the human error cannot be avoided. The aim of this research is to classify the heart disease problem. The study of Backpropagation Neural Network (BPNN) is performed in order to achieve the purpose of this research. The objective of this research is the implementation of BPNN and the evaluation of result of classification of heart disease after the implementation is done. The research methodology of this research consists of six fundamental step which are literature review, data collection, data normalization, BPNN design, BPNN training and BPNN testing and verification. From the research methodology, the heart disease classification using BPNN is discussed and the formulas that need to be computed in order to get the accurate result. For BPNN testing and verification, 30% from the dataset of statlog heart disease taken from University of California-Irvine Machine Learning is used. The other 70% of the dataset is used for BPNN training. In order to classify the heart disease, a Matlab neural network toolbox called nprtool is used in the experiment. After the training and testing process is done, the confusion plot is generated and it shows the training, testing and overall result of classification. In this research, the result indicates that BPNN produced better result than method that used Fuzzy Rule and Hidden Naïve Bayes.

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Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB PEKAN	FSKKP .A85 2016 r Bc. (Browse shelf(Opens below))	1	Not for loan		0000117582
Final Year Report	UMPLIB PEKAN	CD 10694 \| FSKKP .A85 2016 r Bc. (Browse shelf(Opens below))	1	Not for loan		0000117583

Faculty of Computer Systems and Software Engineering

Project paper (Bachelor of Computer Science (Computer Systems & Networking)) With Honours) -- Universiti Malaysia Pahang – 2016

Bibliography : p. 39-41

Normally, a heart disease patient is diagnosed with the disease at the hospital. Nowadays, there is no system available yet to diagnose a patient with heart disease in a fast pace. It is becoming necessary and crucial for human kind nowadays especially in Malaysia to have a solution for a faster diagnosis of heart disease. When a patient is diagnosed at the hospital, it is a time consuming as the complexity of data that has many attributes and doctor or physician is the only professional people that need to analyse the data. Besides, the high chances of fault diagnose may occur as the human error cannot be avoided. The aim of this research is to classify the heart disease problem. The study of Backpropagation Neural Network (BPNN) is performed in order to achieve the purpose of this research. The objective of this research is the implementation of BPNN and the evaluation of result of classification of heart disease after the implementation is done. The research methodology of this research consists of six fundamental step which are literature review, data collection, data normalization, BPNN design, BPNN training and BPNN testing and verification. From the research methodology, the heart disease classification using BPNN is discussed and the formulas that need to be computed in order to get the accurate result. For BPNN testing and verification, 30% from the dataset of statlog heart disease taken from University of California-Irvine Machine Learning is used. The other 70% of the dataset is used for BPNN training. In order to classify the heart disease, a Matlab neural network toolbox called nprtool is used in the experiment. After the training and testing process is done, the confusion plot is generated and it shows the training, testing and overall result of classification. In this research, the result indicates that BPNN produced better result than method that used Fuzzy Rule and Hidden Naïve Bayes.