Design and Implementation of Heart Disease Prediction System using Expert System and Data Mining

Emeka Okorie,  Anyaragbu Hope, Chukwura Somto, Okoh Chris and Ejikeme Akachukwu

Department of Computer Science Tansian University Umunya, Nigeria.


Heart Disease is a class of disease that involves the heart or the blood vessels. It is the second leading cause of death in world for men and women. In order to predict/ diagnose any disease, the expert system designed by human may be a cheering way out to diminish cost, time, human efforts and medical error. Data Mining is quickly expanding in a wide range of applications nowadays. Medical data mining is an important data mining field. In healthcare, there is a multitude of data available, but no effective analysis tool exists to uncover hidden links in data. Despite the fact that millions of people die from heart disease each year, the use of data mining techniques in heart disease diagnostics appears to be critical. Discovering knowledge can aid doctors in the diagnosis of cardiac disease. The objective of this study is to “Design and implement Heart Disease Prediction System using Expert System and Data Mining”. The software is an expert system with database containing an expert knowledge. The study was guided by five specific objectives. The proposed system is a web-based application system for heart disease prediction that has the potential to address the issue of the linked heart disease.  This work, Heart Disease Prediction system using Expert System and Data Mining is considered to be one of the most powerful tools for assistance in the hospital and healthcare facility. I am motivated to create an expert system to identify heart disease in this research work since we need such a crucial tool. The methodology adopted in analysis of the system is Extreme Programming (XP) Model. XP is a disciplined approach to delivering high-quality software quickly and continuously. The database was designed and implemented using MySQL as database platform for storing the knowledge. HTML, CSS and JavaScript were used to develop both the user and administrator interfaces of the system while PHP was used as the server side scripting language to implement the functionality of the system.

Keywords: Design, Implementation, Heart, Disease, Prediction System and Expert System  


People nowadays work really hard to make a lot of money in order to live lavish lives. As a result, people neglect to care for their health. Because of this, the food that they eat has changed. Their alterations in lifestyle ultimately result in early onset diabetes, hypertension, and other ailments. All of these factors cause people to neglect their health, which raises the risk of heart disease. The heart is the most important organ in the human body, and when it is harmed, the other main organs of the body are also impacted. Consequently, there has been a significant growth in the use of computer technology in the medical industry for disease diagnosis and patient care. Despite the enormous complexity and unpredictability of these sectors, which involve computers, intelligent systems like artificial neural networks, genetic algorithms and machine learning have been created. The many and ambiguous risk factors for heart disease might make it challenging for doctors to diagnose the condition. The physician’s work is challenging since there are so many variables to consider while analysing and diagnosing the patient’s problem. Therefore, specialist needs a precise instrument that takes these risk variables into account and produces prediction results over a wide range of time. Computer-based methods are increasingly used to improve the quality of medical services. Artificial Intelligence (AI) is the area of computer science focusing on, creating machine that can engage on behaviours that humans consider intelligent. The proposed system is dealing with problem of heart disease diagnosis as an expert system. An expert system is that employs human knowledge captured in a computer to solve problems that ordinarily required human expertise. Expert system seeks and utilizes relevant information from their human users and available knowledge based in order to make recommendations. Recently, Machine learning has increasingly important in the field of healthcare Beguma, Siddiqueb and Tiwaric (2021). It is a technique that allows machine to mimic human behaviour by repeating it. It enables machine to learn from their experiences (training data) without being programmed, allowing them to predict desired elements. Remote Healthcare technologies can also be utilized to integrate decision making tools onto mobile devices. It can collect data from patients in real-time and provide health services efficiently. It allows patients to be monitored without having to attend hospitals or health clinics Rani, Kuma, Sid Ahmed and Jain (2021). The health is an important part of the human body because it supplies pure blood to all regions of the body. People cannot live for a second if their hearts are not in good operating order. Heart failure typically happens when the heart is unable to send the necessary amount of blood to other regions of the human body in order for the body to function normally. According to a survey conducted by the World Health Organization, 80% of persons died as a result of a health attack each year (WHO). Heart disease has becomes one of the world’s most dangerous human ailments Sharmaa, Shambhub, Dash and Sakshid (2021). Some individuals are predisposed to heart disease from birth. Many variables raise the chance of developing heart disease, and most individuals seek to lower those risks. The elements in this situation are: 

  • Having diabetes which is a strong risk for heart disease.
  • Substance abuse such as a cocaine
  • Being overweight
  • Not getting enough exercise and fee depressed or having excess stress
  • Smoking
  • High blood pressure increases the risks of heart disease and heart failure
  • Excess cholesterol in blood builds up inside walls of heart’s arteries (blood vessels).


This research is web based application for Heart Disease Prediction System. The system is user friendly, economical and efficient, which allows a patient or user to interact with a computer and mobile application. As a web based application, the designed system is limited and can only be utilized in environment with Internet access. In this system, the username and password are sent by the web application to the server, where they are then processed to authenticate the application’s credentials by verifying the username and password that have been registered with the server. If the destination exists, the information is then processed and delivered there. The user is supposed to choose from a list of heart disease symptoms when the system is consulted, including wheezing, chest discomfort, shortness of breath, diabetes, weariness, and obesity. It performs a diagnosis by consulting its knowledge base, which has certain pre-programmed indications and symptoms linked to particular specific illnesses, helpful resources, the name of the illness, and a medication prescription for it. The system has been able to accurately diagnose common heart disease ailment such as Coronary Artery and can be upgraded to diagnose other disease not presently catered for.


In the light of our finding in the course of this research work, it may be pertinent to make the following recommendation:

  1. The hardware and software to be used for the new system should satisfy the hardware and software requirements mentioned in chapter four, and should be in good efficient conditions to avoid unnecessary breakdown.
  2. The computerization effort should gradually be extended to cover all forms of diseases.
  3. The computerization effort should also gradually be extended to cover all operations and other forms of diagnoses in hospital/healthcare as indicated in the study.
  4. This project (application) is highly recommended for any medical organization, and the end-users/staff should be trained on how to use it, and is open to improvements. The pilot changeover method should be used in implementing this system.


  1. Beguma, S., Siddiqueb, F.A. and Tiwaric, R. (2021)A Study for Predicting Heart Disease Using Machine Learning. Turkish Journal of Computer and Mathematics Education,    12, 4584-4592 
  2. Rani, P., kumar, R., Sid Ahmed, N.M.O. and Jain, A. (2021) A Decision Support System for
  3. Heart Disease Prediction Based upon Machine Learning. Journal of Reliable
  4. Intelligent Environments, 7,263-275. https//
  5. Sharmaa, C., Shambhub, S.,Dasb, P. and Sakshid, S.j. (2021) Feature Contributing towards
  6. Heart Disease Prediction Using Machine Learning. Workshop on Advanced in
  7. Computational Intelligence at ISIC 2021, Delhi, 25-27 February 2021, 84-92
  8. Baglione, L. (2012) Writing a research paper in political science “Thousand Oak” Califonia CQ prints.Carlos Ordonez, “Improving Heart Disease Prediction Using Constrained Association Rules,” Seminar Presentation at University of Tokyo, 2004
  9. Patil, P.S. (2021) Automated Heart Disease Recognition System. International Journal of Research in Engineering and Science, 9, 18-23.
  10. Choudhary, G. and Singh, S.N. (2020) Prediction of Heart Disease Using Machine Learning Algorithms. 2019 1st International Conference on Innovation in Information and Communication Technology (ICIICT), Chennai, 25-26 April 2019. 197-202.
  11. Almustafa, K.M. (2020) Prediction of Heart Disease and Classifiers’ Sensitivity Analysis. BMC Bioinformatics, 21, Article No. 278. https//
  12. Maurer, F. and Martel, S. (2019), Extreme Programming: Rapid Development for Web-basedApplications, IEEE Internet Computing, 6(1), pp.86-9111, Jan/Feb 2002
  13. Ali, M.M., Paul.B.K., Ahmed, K,.Bui, F.M., Quinn, J.M.W. and Moni, M.A. (2021) Heart Disease Prediction Using Supervised Machine Learning Algorithms: Performance Analysis and Comparison. Computer in Biology and Medicine, 136, Article ID: 104672.

CITE AS:Emeka Okorie, Anyaragbu Hope, Chukwura Somto, Okoh Chris and Ejikeme Akachukwu (2023). Design and Implementation of Heart Disease Prediction System using Expert System and Data Mining. NEWPORT INTERNATIONAL JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES (NIJSES) 3(3):149-166