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HEART DISEASE PREDICTION PROJECT
ABSTRACT
Heart Disease according to the survey is the leading cause of death all over the world. The health sector has a lot of data, but unfortunately, these data are not well utilized. This is a result of lack of effective analysis tools to discover salient trends in data. Data Mining can help to retrieve valuable knowledge from available data. It helps to train model to predict patients’ health which will be faster compared to clinical experimentation. A Different Implementation of machine learning algorithms such as K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Naïve Bayes, etc. have been applied but there has been limit to modeling using Bayesian Belief Network. This research tackles this drawback. Here, we propose a web application that allows users to get instant guidance on their heart disease through an intelligent system online. The application is fed with various details and the heart disease associated with those details. The application allows user to share their heart related issues. It then processes user specific details to check for various illness that could be associated with it. Based on result, the can contact doctor accordingly for further treatment. The system allows user to view doctor’s details too. The system can be used for free heart disease consulting online. The model has an accuracy of 85%, precision of 86%, recall of 85% and f1-score of 85%. It was concluded that the model outperformed Naïve Bayes classifier which have accuracy of 80%, precision of 81%, recall of 80% and f1-score of 80%.
CHAPTER ONE
INTRODUCTION
1.1 Research Background
The heart is a vital organ in the human body. It is responsible for pumping blood through the blood vessels of the circulatory system. The blood helps to convey oxygen which is needed for the functioning of the body cells. The heart beats for about 100,000 times per day. Heart diseases are also called cardiovascular diseases (CVDs). Heart diseases happen to be the most common cause of death globally. According to WHO, both men and women are equally affected by heart disease. WHO estimated that 17.9 million people are dead due to heart disease in 2016 which represent 31% of all global deaths. 85% of these deaths are caused by stroke and heart attack (WHO, 2016).
Heart disease or cardiovascular disease is the class of diseases that involve the heart or blood vessels (arteries and veins). Today most countries face high and increasing rates of heart disease and it has become a leading cause of debilitation and death worldwide in men and women over age sixty-five and today in many countries heart disease is viewed as a “second epidemic,” replacing infectious diseases as the leading cause of death (Gale Nutrition Encyclopedia, 2011).
Cardiovascular diseases result when the heart and blood vessels are not working normally. Other problems do exist along with the cardiovascular disease. Arteriosclerosis which generally means hardening of arteries, the arteries, in this case, becomes thicker and inflexible. Atherosclerosis means narrowing of arteries, so less blood flow through the buildups (Varun, Mounika, Sahoo, & Eswaran, 2019). Heart attacks occur generally when the blood clots or there is a blockage to blood flow from the heart.
To buttress the importance of overcoming deaths of cardiovascular diseases, WHO launched a new program on 22nd September 2016 called the Global Hearts (WHO, 2017).
Traditionally, heart disease was thought to be the problem of developed countries, but now it is becoming a headache for developing countries too and it is especially devastating for the developing countries since they do not have adequate health care (WHO report 2006). Some factors that tend to prone heart diseases are smoking, high cholesterol, high blood pressure, physical inactivity, unhealthy diet, obesity, and poorly controlled diabetes (Musa 2010).
In recent years, Data mining has found its significant hold in every field including health care. Mining process is more than the data analysis which includes classification, clustering, and association rule discovery. It also spans other disciplines like Data Warehousing, Statistics, Machine learning and Artificial Intelligence (Larose, 2005). Data mining can be a useful tool in the health sector and healthcare. Organizations that perform data mining are better positioned to meet their long-term needs, Benko and Wilson (2003) argue that data can be a great asset to healthcare organizations, but they have to be first transformed into information.
Predicting the outcome of a disease is one of the most interesting and challenging tasks in which to develop data mining applications. In recent years new research avenues such as knowledge discovery in databases, which includes data mining techniques, has become a popular research tool for medical researchers who seek to identify and exploit patterns and relationships among large number of variables, and be able to predict the outcome of a disease using the historical cases stored within datasets.
Kangwanariyakul et al., (2010), Patil and Kumaraswamy, (2009) have tried to apply data mining techniques in the diagnosis of heart disease. Different classification methods such as Neural Networks and Decision Trees were applied to predict the presence of heart disease and to identify the most significant factor which contributes for the cause of the disease, while association rule discovery was used to identify the effect of diet, lifestyle, and environment on the outcome of the disease. Clustering algorithms like the k-means algorithm were used on heart disease data warehouse which contains screening clinical data of patients to identify instances which are more relevant to heart attack. The results showed a bright future of data mining in the diagnosis of heart disease.
Data mining helps to identify useful trends in a large set of data. As a result of the increase in the amount of health data gathered through the electronic health record (EHR) systems, it is believed that strong analysis tools are important. With a huge amount of data, health care providers are now optimizing the efficiency of their organization using data mining. Data mining has helped the health care industry to specifically reduce costs by increasing efficiencies, improving patient’s quality of life, and most importantly saving the lives of more patients. In healthcare, data mining has proven effective in areas such as predictive medicine, customer relationship management, detection of fraud and abuse, management of healthcare and measuring the effectiveness of certain treatments. Data mining can be applied to health data for many different purposes and investigations. These applications can roughly be grouped into the four main categories as discussed below (Tekieh & Raahemi, 2015).
Hence, this thesis intends to develop a heart disease prediction system using data mining technique.
1.2 Problem Statement
In order to decrease mortality from heart diseases there should be a fast and effective detection method especially, in developing countries like Niheria where there is a shortage of specialists and wrongly diagnosed cases are high. Data mining can be a convenient tool to assist physicians in detecting the disease by obtaining knowledge and information regarding the disease from patient’s data.
Early diagnosis and treatment of heart disease is vital to preventing serious, even life threatening complications such as cardiac arrest and death, but due to mistakes made during the analysis of the measurements that are taken during the echocardiography examination diagnosis of heart disease may be overlooked or delayed. As Patil and Kumaraswamy, (2009) noted like other diseases detection of heart diseases is a multi-layered issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable option.
The purpose of this study is, therefore to develop a reliable system for heart disease prediction. As a result of some risks identified with clinical treatments such as the delay in the result and the non-availability of the medical facilities to the people, the prediction model is recommended. Although prediction model is not alternative to clinical treatments, but it can serve as first hand tool to be aware of any type of disease and be prepared for it.
1.3 Research Aim and Objectives
The general objective of this study is to design a predictive model for heart disease detection using a bayelsian network model. It is expected that the Bayesian network model will assist in making inference about heart diseases, thereby serving as a diagnostic tool to support the medical practitioners.
1.4 Scope and Limitation of the Study
This study focuses on designing a predictive model for heart disease detection using a bayelsian network model. The proposed system is intended to be capable of predicting heart disease cases in general (i.e. specific heart disease types could not be identified using the prediction model developed by this study).
1.5 Significance of the Study
It is envisioned that the results of this study will reduce medical errors, enhance patient safety and reduce mortality rate from heart disease. In addition, other medical institutions both private and public can use the result of this study for their medical decisions related to heart disease diagnosis. Furthermore, Researchers from the medical science and IT fields can use the result of this research as an input to their study. It will also help them to come up with better solutions to the problems facing heart disease diagnosis using echocardiography.
Finally, the public will get a proper medical care if the result of this study is used along with the existing system.
1.6 Organization of the Thesis
The thesis contains five basic chapters.
Chapter 1 discusses the introductory part of heart diseases, problem statement, aim, objectives, the expected contribution and the thesis structure.
Chapter 2 gives an insight into an overview of machine learning, Bayesian Network, and
critically review the literature.”
Chapter 3 discusses the research methodology used as well as the network design. The data preprocessing steps and the tools used for the study were also described.
Chapter 4 provides a detailed discussion on the results and system implementation.
Chapter 5 rounds off the research by giving the conclusion.
