Heart Diseases Prediction and Diagnosis using Supervised Learning

Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequate preventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors. Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures are inadequate. In today’s digital world, several clinical decision support systems on heart disease prediction have been developed by different scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decision support systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques. Classification algorithms such as the Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widely employed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in the creation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporate multiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.

Data mining techniques have been widely used in clinical decision support systems for prediction and diagnosis of various diseases with good accuracy. These techniques have been very effective in designing clinical support systems because of their ability to discover hidden patterns and relationships in medical data. One of the most important applications of such systems is in diagnosis of heart diseases because it is one of the leading causes of deaths all over the world. Almost all systems that predict heart diseases use clinical dataset having parameters and inputs from complex tests conducted in labs. None of the system predicts heart diseases based on risk factors such as age, blood pressure, fasting blood sugar, chest pain etc. Heart disease patients have lot of these visible risk factors in common which can be used very effectively for diagnosis. System based on such risk factors would not only help medical professionals but it would give patients a warning about the probable presence of heart disease even before he visits a hospital or goes for costly medical check-ups. Hence this paper presents a technique for prediction of heart disease using major risk factors. This technique involves two most successful data mining tools, Support vector machine and Principal component analysis. The hybrid system implemented uses the global optimization advantage PCA for initialization of neural network weights. The learning is fast, more stable and accurate as compared to back propagation.

Cardiovascular sickness is the largest cause of death in developing countries. The study of this coronary heart disease prediction model using a data mining technique and decision tree algorithm are applied in medical research, especially in heart disease prediction. The Coronary Heart Disease is also known as Coronary Artery Disease (CAD). Hence the decision system is analyses the heart disease for the patient. In this paper coronary illness studied a more number of input attributes and database records based on the patient's clinical data. This paper focuses on the around the prediction of heart disease accuracy value using the decision tree algorithm.

In modern society, mortality and morbidity are caused majorly by heart disease (HD), and in world, deaths are mainly caused by heart disease (HD). The detection of HD and prevention against death is a challenging task. Medical diagnosis is highly complicated and it is very important. It must be performed efficiently with high accuracy. The professionals in healthcare in heart disease diagnosis are assisted by using various techniques in data mining. In this work, heart disease prediction method with following steps is introduced. The steps are preprocessing technique, feature selection, and learning algorithm. Before that important features are selected via the use of the updated frequency-based bat algorithm (UFBBA). In the UFBBA algorithm, the frequency values are computed via the use of the features. If the features are most important, then the frequency is higher else the frequency is lower. A selected feature from the UFBBA is used for better accuracy results than the other classifiers. A feature selected from the algorithm is applied for classification (Vote). Experimentation dataset of the proposed system is collected from Irvine (UCI) Cleveland dataset, University of California dataset. The results are measured with respect to accuracy, f-measure, precision, and recall.

Heart disease is one of the leading cause deaths of worldwide. Prediction of cardiovascular infection is a critical challenge in the area of clinical data analysis. Data mining techniques are providing an effective decision and significant results on data that are used widely for predicting. The purpose of this paper is to propose a novel approach with aims to find a noteworthy method to diagnose heart disease prediction. In this research, a unique dataset was created by combining the Cleveland dataset and Stalog heart disease datasets collected from the UCI ML repository. The new dataset contains 14 medical parameters such as age, sex, blood pressure, and 568 instances for training and prediction heart disease. This paper offers a novel methodology of NNDT (Neural Network and Decision Tree) that uses Neural Network for training model and Decision Tree to test classification for better heart disease prediction. The performance of the proposed approach have been compared with Naive Bayes, Support Vector Machine, Neural Network, Voted Perceptron, and Decision Tree algorithms. The results showed that the accuracy and performance improved as compared to other techniques and methods. This study enables the researchers to analyze the heart disease data with a new approach to predict heart diseases to maintain human health.

<p>For the past two decades, heart disease has been classified as one of the main causes of mortality globally. Fortunately, most researchers focused on data mining techniques, which play an important role in accurately predicting heart disease to develop their models. In this paper, by combining particle swarm optimization (PSO) and modified random forest (MRF), a new approach (PSO-MRF) is proposed to predict heart disease. The main purpose is to select the important features after the bootstrap method for each decision tree in the random forest, and then optimize the MRF by the PSO algorithm. The experiments are carried out using the publicly accessible UCI heart disease datasets. Thorough experimental analysis demonstrates that our approach has outperformed the random forest algorithm as well as many other classifiers. This model helps doctors and researchers improve the diagnosis and treatment of heart disease, resulting in more prompt, accurate patient care.</p>

Heart disease has long been a major threat to human health, causing about one-third of all deaths each year. Therefore, there is a need for accurate and effective prediction of cardiac disease using machine learning techniques. This paper uses data from 1,319 patients with heart disease and applies several machine-learning methods to study the relationship between a total of eight factors. The algorithms used in this paper include neural networks, decision trees, random forests, and so on. The best model was established by the random forest method, with an accuracy of 96.97%, precision of 97.03%, recall of 96.97%, and f1-Score of 0.9. It was found that troponin and CK-MB indicators had the highest influence weights, and the sum of the weights of each model accounted for more than 75% of the total weight, which demonstrated their significance in the prediction of heart disease, and the results could be utilized for the future prediction of heart disease. In addition, it also plays an essential role in the prediction of heart disease. To sum up, this is really important for the prediction of future heart disease.

Heart disease is a leading cause of mortality globally and its prevalence is increasing year after year. Recent statistics from the World Health Organization show that about 17.9 million individuals are embattled with heart diseases annually and people under the age of 70 account for one-third of these deaths. Hence, there is need to intensify research on early heart disease prediction and artificial intelligence-based heart disease prediction systems. Previous heart disease prediction systems using machine learning techniques are unable to manage large amount of data, resulting in poor prediction accuracy. Hence, this research employs Convolutional Neural Networks, a deep learning approach for prediction of heart diseases. The dataset for training and testing the model was obtained from a government owned hospital in Nigeria and Kaggle. The resulting system was evaluated using precision, recall, f1-score and accuracy metrics. The results obtained are: 0.94, 0.95, 0.95 and 0.95 for precision, recall, f1-score and accuracy respectively. This show that the CNN-based model responded very well to the prediction of heart diseases for both negative and positive classes. The results obtained were also compared to some selected machine-learning models like Random Forest, Naïve Bayes, KNN and Logistic Regression and results show that the developed model achieved a significant improvement over the methods considered. Therefore, convolutional neural network is more suitable for heart disease prediction than some state-of-the-art machine-learning models. The contribution to knowledge of this research is the use of Afrocentric dataset for heart disease prediction. Future research should consider increasing the data size for model training to achieve improved accuracy.

Several researchers have developed intelligent medical devices to support the systems and further to enhance the ability to diagnose and predict heart diseases. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researchers assessed that how to use ensemble model, which proposes a more stable performance than the use of base learning algorithm and these leads to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher developed the meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, as per the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has the high prediction probability score in the implementation of heart disease prediction.

In recent years, the incidence rate of various types of heart disease is increasing, so more and more studies focus on the influencing factors and prediction of heart disease. This paper investigates the relationship between heart disease and 21 representative variables to explore risk factors and predict the heart attack. First, through comparative analysis, we find that there is a significant difference in the distribution of variables. The findings highlight factors associated with heart disease, such as underlying diseases, lifestyle habits, and medical conditions. Secondly, these four factors (health condition, environment, age, and BMI) are extracted through factor analysis. Thirdly, a binary logistic regression prediction model is established to study the relationship between variables. Finally, the combination of multiple analysis methods in this study is a novel approach that can be used for exploring heart disease factors and predicting disease onset.

Studies have shown that heart diseases have emerged as the number one cause of deaths. Heart disease is accountable for deaths in all age groups and is common among males and females. A good solution to this problem is to be able to predict what a patient's health status will be like in the future so the doctors can start treatment much sooner which will yield better results. It's a lot better than acting at the last minute where the patient is already at risk and hence the prediction of heart disease is widely researched area. A lot of research and technological advancement has been recorded in similar fields. This paper aims to report about taking advantage of the various data mining techniques and develop prediction models for heart disease survivability.

Background: Heart diseases continue to be among the primary causes of morbidity and mortality worldwide, highlighting the need for novel diagnostic methods to accurately, early, and economically predict and diagnose heart diseases. Current physical explores upon expert knowledge, possibly missing intricate and non-linear associations between heart disease risk factors. This is where machine learning (ML)methods emerge as promising alternatives for prediction and diagnosis of heart diseases by emphasizing data availability. Methods: This paper attempts to find out how six popular machine learning algorithms namely, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGB) perform on a publicly available heart disease dataset collected from Kaggle. For better performance and to make them comparable to each other, basic preprocessing techniques have also been implemented. The performance of all these algorithms will be measured by their respective definitions of accuracy, precision, recall, and F1 score. Result: With an F1-score of 98.52%, Random Forest and XGBoost surpassed other models, yielding a superior accuracy in predictions, measuring 98.53%. The models recorded a perfect level of precision, 100%, coupled with high values of recall, exceeding 97%. Gradient Boosting recorded impressive results, receiving an accuracy score of 93.17%. Conclusion: The research findings revealed that the Random Forest and XGBoost models are very effective in the prediction and diagnosis of heart disease. These two models have the capacity to support a clinical decision. Novelty: That is to say, the novelty of this research is that it evaluates multiple machine learning models with comparison to each other. The role of machine learning in enhancing cardiovascular disease diagnosis as well as decision support systems in healthcare and educating and aware the people about heart care and health.

Heart plays significant role in living organisms. Diagnosis and prediction of heart related diseases requires more precision, perfection and correctness because a little mistake can cause fatigue problem or death of the person. The prediction of heart disease is critically significant for diagnosis of diseases and treatment. The data mining techniques that can be applied in medicine, and in particular some machine learning techniques including the mechanisms that make them better suited for the analysis of medical databases. Heart disease is a significant problem in recent times; the main reason for this disease is the intake of alcohol, tobacco, and lack of physical exercise.

The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.

Recent time’s heart disease has been propagating at an intensified rate and has become a large cause of untimely deaths all over the world among non-communicable diseases. South Asian countries in particular seem to have a higher risk of heart disease suggest the studies. It is challenging to predict heart disease, the expertise required to do this is not easy to get and only highly experienced doctors have it. A large amount of data is available to us which can provide us with hidden information, therefore data analysis techniques can be used to make some effective decisions in this area. Not only would this provide us with the reliable way of predicting heart disease, but it would also reduce the pressure on the medical professionals. These algorithms can estimate the likelihood of a person developing heart disease based on factors such as age, gender, blood pressure, stress, and so on. We used a data set with 1025 samples of data including 13 attributes in our research. In this work, we used the Logistic Regression, Decision Tree, SVM, Naive Bayes, Random Forest, and KNN algorithms to predict heart disease, with the purpose of determining which method is the most reliable. In our case, Decision Tree came out at the top with 98.53% accuracy. In the end, we also compared the results of some other study with ours.