# Effective Heart Disease Prediction Using Machine Learning Techniques
## 1 Introduction  
Globally, cardiovascular disease (CVDs) is the primary cause of morbidity and mortality, accounting for more than 70% of all fatalities. According to the 2017 Global Burden of Disease research, cardiovascular disease is responsible for about 43% of all fatalities [1, 2]. Common risk factors for heart disease in high-income nations include lousy diet, cigarette use, excessive sugar consumption, and obesity or excess body fat [3, 4]. However, low- and middle-income nations also see a rise in chronic illness prevalence [5]. Between 2010 and 2015, the global economic burden of cardiovascular diseases was expected to reach roughly USD 3.7 trillion [6, 7] ([PERSON] et al., 2015; [PERSON] et al., 2019). In addition, technologies such as electrocardiograms and CT scans, critical for diagnosing coronary heart disease, are sometimes too costly and impractical for consumers. The reason mentioned above alone has resulted in the deaths of 17 million people [5]. Twenty-five to thirty percent of firms' annual medical expenses were attributable to employees with cardiovascular disease [8]. Therefore, early detection of heart disease is essential to lessen its physical and monetary cost to people and institutions. According to the WHO estimate, the overall number of deaths from CVDs would rise to 23.6 million by 2030, with heart disease and stroke being the leading causes [9]. To save lives and decrease the cost burdenon society, it is vital to apply data mining and machine learning methods to anticipate the chance of having heart disease. Heart disease, specifically cardiovascular disease (CVDs), is a leading cause of morbidity and mortality worldwide, accounting for over 70% of all global deaths. According to the Global Burden of Disease Study 2017, CVD accounts for more than 43% of all deaths. Common risk factors associated with heart disease include unhealthy food, tobacco, excessive sugar, and overweight or extra body fat, often found in high-income countries. However, low- and middle-income countries are also seeing an increase in the prevalence of chronic diseases. The economic burden of CVDs worldwide has been estimated to be approximately USD 3.7 trillion between 2010 and 2015. Furthermore, devices such as electrocardiograms and CT scans, essential for detecting coronary heart disease, are often too expensive and infeasible for many low- and middle-income countries. Therefore, early determination of heart disease is crucial to decrease its physical and financial burden on individuals and organizations. According to a WHO report, by 2030, the total number of deaths due to CVDs will increase to 23.6 million, mainly from heart disease and stroke. Therefore, it is crucial to use data mining and machine learning techniques to predict the likelihood of developing heart disease in order to save lives and reduce the economic burden on society. In the medical field, a vast amount of data is generated daily using data mining techniques, and we can find hidden patterns that can be used for clinical diagnosis [10]. Therefore, data mining plays a vital role in the medical field, which can be proved by the work conducted in the past few decades. Many factors, such as diabetes, high blood pressure, high cholesterol, and abnormal pulse rate, need to be considered when predicting heart disease [11]. Often, the medical data available need to be completed, affecting the results in predicting heart disease. Machine learning plays a crucial role in the medical field. Using machine learning, we can diagnose, detect, and predict various diseases. Recently, there has been a growing interest in using data mining and machine learning techniques to predict the likelihood of developing certain diseases. The already-existing work contains applications of data mining techniques for predicting the disease. Although some studies have attempted to predict the future risk of the progression of the disease, they have yet to find accurate results [12].