UUM Staff Develops A Diagnostic Tool For Early Detection Of Cardiovascular Disease

UUM STAFF DEVELOPS A DIAGNOSTIC TOOL FOR EARLY DETECTION OF CARDIOVASCULAR DISEASE

UUM ONLINE: Rapid advancement in technology, coupled with time limitations as well as bottlenecks that many cardiovascular patients have to face regularly at most hospitals have encouraged a Tutor at the School of Computing, Mohamad Sabri Sinal @ Zainal, 28, to develop a new mechanism using the computer system called the 'Automatic Heart Disease Detecting Using ECG Signals' to perform analysis on the data of cardiovascular patients without having to rely solely on the doctors at all time.

In general, the Electrocardiogram (ECG) provides useful reference in identifying the condition of the human heart from multiple angles and various types of critical illness that are associated with heart problems which require immediate treatment if any irregularities are detected in the pulse of the heart. The procedures used for identifying the heart conditions are very complicated and the process of analysing the result can only be performed by a doctor or a cardiologist.

Mohamad Sabri said, the process to diagnose heart conditions and to detect any irregularities in the heart's activity through the introduction of computerised artificial intelligence will help to solve complex problems effectively, especially in the medical field in which the result of the data analysis must have a very high level of accuracy.

"There are important data in a patient’s ECG and this has become the main focus in analysing the condition of the heart so that any irregularities involving the heart can be detected at an early stage. Thus, in this two-year study, the data from more than 30 healthy and unhealthy subjects were collected from hospitals for the purposes of performing computerised experiments.

"I have introduced four (4) mechanisms for diagnosing heart conditions using the computerised approach i.e. DP-Matching technique with Algorithm; Artificial Neural Network; statistical analysis using the Correlation Coefficient with Pearson Model and Algorithm,” said Mohamad Sabri who has recently graduated with a Master’s degree from the Shibaura Institute of Technology, Tokyo, Japan.

"To date, the Algorithm is a new approach which has never been introduced by any researchers. The accuracy level in tracing the patient’s ECG data is almost 100 percent.

Mohamad Sabri added that two of the mechanisms introduced in the diagnostic procedures have been awarded with a high 'novelty' level at the International Conference on Innovation in Medicine and Healthcare 2015 in Kyoto.

He said the results of the study using the actual patients’ data from the hospitals have proven that artificial intelligence is capable of solving some major problems in human life, especially in monitoring the health and well-being more efficiently.
 
Mohamad Sabri graduated with a Bachelor of Information Technology from UUM and he will pursue his PhD in Japan. He planned to introduce a hybrid method that can be used to automatically identify the risk of stroke at an early stage through the early detection of the symptoms by the computer using the patients’ ECG data.

Translated by Nur Ida Faradilla binti Aziz

 

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