# Effective Heart Disease Prediction Using Machine Learning Techniques
## 2 Literature Survey  
In recent years, the healthcare industry has seen a significant advancement in the field of data mining and machine learning. These techniques have been widely adopted and have demonstrated efficacy in various healthcare applications, particularly in the field of medical cardiology. The rapid accumulation of medical data has presented researchers with an unprecedented opportunity to develop and test new algorithms in this field. Heart disease remains a leading cause of mortality in developing nations [12; 13; 14; 15; 16], and identifying risk factors and early signs of the disease has become an important area of research. The utilization of data mining and machine learning techniques in this field can potentially aid in the early detection and prevention of heart disease. The purpose of the study described by [PERSON] et al. (2016) [17] is to create an innovative machine-learning-based cardiovascular disease (CVD) prediction system in order to increase the precision of the widely used Framingham risk score (FRS). With the help of data from 689 individuals who had symptoms of CVD and a validation dataset from the Framingham research, the proposed system--which uses a quantum neural network to learn and recognize patterns of CVD--was experimentally validated and compared with the FRS. The suggested system's accuracy in forecasting CVD risk was determined to be 98.57%, which is much greater than the FRS's accuracy of 19.22% and other existing techniques. According to the study's findings, the suggested approach could be a useful tool for doctors in forecasting CVD risk, assisting in the creation of better treatment plans, and facilitating early diagnosis. In a study conducted by [PERSON] et al. (2020) [18], the authors aimed to develop a model for predicting cardiovascular disease using machine learning techniques. The data used for this purpose were obtained from the Cleveland heart disease dataset, which consisted of 303 instances and 17 attributes, and were sourced from the UCI machine learning repository. The authors employed a variety of supervised classification methods, including naive Bayes, decision tree, random forest, and k-nearest neighbor (KKN). The results of the study indicated that the KKN model exhibited the highest level of accuracy, at 90.8%. The study highlights the potential utility of machine learning techniques in predicting cardiovascular disease, and emphasizes the importance of selecting appropriate models and techniques to achieve optimal results. In a study by [PERSON] et al. (2022) [2], the objective was to use machine learning (ML) techniques to identify the most significant risk variables for cardiovascular disease (CVD) in patients with metabolic-associated fatty liver disease (MAFLD). Blood biochemical analysis and subclinical atherosclerosis assessment were performed on 191 MAFLD patients. A model to identify those with the highest risk of CVD was built using ML approaches, such as multiple logistic regression classifier, univariate feature ranking, and principal component analysis (PCA). According to the study, hypercholesterolemia, plaque scores, and duration of diabetes were the most crucial clinical characteristics. The ML technique performed well, correctly identifying 40/47 (85.11%) high-risk patients and 114/144 (79.17%) low-risk patients with an AUC of 0.87. According to the study's findings, an ML method is useful for detecting MAFLD patients with widespread CVD based on simple patient criteria. In a study published by [PERSON] (2019) [19], the author aimed to investigate the utility of machine learning (ML) techniques for predicting heart failure disease. The study utilized a dataset from the Cleveland Clinic Foundation, and implemented various ML algorithms, such as decision tree, logistic regression, random forest, naive Bayes, and support vector machine (SVM), to develop prediction models. A 10-fold cross-validation approach was employed during the model development process.