Heart Disease Prediction using Machine Learning (original) (raw)
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Using Machine Learning to Predict Heart Disease
WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE, 2021
Heart Disease has become one of the most leading cause of the death on the planet and it has become most life-threatening disease. The early prediction of the heart disease will help in reducing death rate. Predicting Heart Disease has become one of the most difficult challenges in the medical sector in recent years. As per recent statistics, about one person dies from heart disease every minute. In the realm of healthcare, a massive amount of data was discovered for which the data-science is critical for analyzing this massive amount of data. This paper proposes heart disease prediction using different machine-learning algorithms like logistic regression, naïve bayes, support vector machine, k nearest neighbor (KNN), random forest, extreme gradient boost, etc. These machine learning algorithm techniques we used to predict likelihood of person getting heart disease on the basis of features (such as cholesterol, blood pressure, age, sex, etc. which were extracted from the datasets. I...
Accurate Prediction of Heart Disease Using Machine Learning: A Case Study on the Cleveland Dataset
International Journal of Innovative Science and Research Technology, 2024
Heart disease remains one of the leading causes of mortality worldwide, with diagnosis and treatment presenting significant challenges, particularly in developing nations. These challenges stem from the scarcity of effective diagnostic tools, a lack of qualified medical personnel, and other factors that hinder good patient prognosis and treatment. The rise in cardiac disorders, despite their preventability, is primarily due to inadequate preventive measures and a shortage of skilled medical providers. In this study, we propose a novel approach to enhance the accuracy of cardiovascular disease prediction by identifying critical features using advanced machine learning techniques. Utilizing the Cleveland Heart Disease dataset, we explore various feature combinations and implement multiple well-known classification strategies. By integrating a Voting Classifier ensemble, which combines Logistic Regression, Gradient Boosting, and Support Vector Machine (SVM) models, we create a robust prediction model for heart disease. This hybrid approach achieves a remarkable accuracy level of 97.9%, significantly improving the precision of cardiovascular disease prediction and offering a valuable tool for early diagnosis and treatment.
Heart Disease Prediction Support System using Machine Learning Approaches
World Wide Journal of Multidisciplinary Research and Development, 2022
Heart disease (cardiovascular disease) is a common source of death in the world and a major health threat. According to WHO research, a cardiovascular disease caused 17.9 million deaths worldwide in 2017. Unfortunately, the mortality and morbidity of cardiovascular disease (heart disease) are increasing year by year, especially in developing countries. According to reports, almost 80% of heatrelated deaths occur in middle-income and low-income countries. In addition, in low-income countries, the age of these deaths is younger than in high-income countries. The poor economic transition in developing countries has led to environmental changes and unhealthy lifestyles; in addition, the aging of the population may increase the risk factors of cardiovascular disease and the incidence of cardiovascular disease (heart disease). The patients and the whole society were hit hard by heart disease. Therefore, strategies for improving the diagnosis and treatment of heart disease are needed in the future. Machine learning may now solve this problem. This study used four different machine learning algorithms to develop and implement a predictive model for heart (cardiovascular) disease detection. The findings of this study show that all developed models, including random forests, decision trees, neural networks, and XGBoost, have high classification accuracy and are similar in predicting heart disease cases. However, the comparison based on the true positive rate shows that the random forest model performs slightly better in predicting heart disease, with a classification accuracy rate of 94.96 %.
HeartCare - A Heart Disease Prediction System based on Machine Learning
Journal of emerging technologies and innovative research, 2021
Heart-related diseases are one of the major causes of deaths across the globe. On an average, one person dies every minute across the world due to cardiovascular diseases. Prediction of heart disease is a difficult task and requires expertise. Therefore, an automated system capable of predicting heart diseases would enhance medical efficiency and reduce the overall cost of treatment. This paper aims to present the concept of the functional model of a heart disease prediction system. This system is highly efficient, built on Django platform and uses a machine learning model. It takes various medical inputs from the user and predicts whether the person has heart disease or not.
IRJET, 2023
Heart disease is a significant health concern, warranting accurate prediction models for timely intervention. This research paper presents a comparative analysis of three popular machine learning algorithms, namely Logistic Regression, Support Vector Machines (SVM), and Random Forest, for heart disease prediction. Utilizing a comprehensive dataset encompassing clinical and lifestyle features, each model was developed and evaluated using standard metrics. The study unveils the most accurate and reliable algorithm for heart disease prediction, offering valuable insights into model performance. Furthermore, feature importance analysis sheds light on critical factors influencing accurate predictions. The results aid healthcare professionals in selecting the most appropriate model for efficient heart disease prediction, contributing to improved patient care and clinical decision-making. Random Forest achieved 88% accuracy, outperforming Logistic Regression and SVM for heart disease prediction.
A Comparative Study on Predicting Cardiovascular Disease Using Machine Learning Algorithms
International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024
Heart disease is a global health concern because of eating patterns, office work cultures, and lifestyle changes. A machine learning-based heart attack prediction system is like having a vigilant watchdog in the medical field. To estimate the danger of a heart attack, it all boils down to analyzing data and complex algorithms. Four primary categories were established at the outset of this study: age, gender, BMI, and blood pressure. The data on heart illness was then classified using a variety of machine learning approaches, including XGBoost Model, Gradient Boosting Model, Random Forest, Logistic Regression, and Decision Trees. The results in terms of accuracy, false positive rate, precision, sensitivity, and specificity were then compared. Results in terms of accuracy, precision, recall, and f1_score were found to be greatest when using Logistic Regression (LR). It is therefore strongly recommended that data on cardiac disease can be classified using the logistic regression technique.
IJERT-Heart Disease Prediction using Machine Learning
International Journal of Engineering Research & Technology (IJERT), 2020
https://www.ijert.org/heart-disease-prediction-using-machine-learning https://www.ijert.org/research/heart-disease-prediction-using-machine-learning-IJERTV9IS040614.pdf In recent times, Heart Disease prediction is one of the most complicated tasks in medical field. In the modern era, approximately one person dies per minute due to heart disease. Data science plays a crucial role in processing huge amount of data in the field of healthcare. As heart disease prediction is a complex task, there is a need to automate the prediction process to avoid risks associated with it and alert the patient well in advance. This paper makes use of heart disease dataset available in UCI machine learning repository. The proposed work predicts the chances of Heart Disease and classifies patient's risk level by implementing different data mining techniques such as Naive Bayes, Decision Tree, Logistic Regression and Random Forest. Thus, this paper presents a comparative study by analysing the performance of different machine learning algorithms. The trial results verify that Random Forest algorithm has achieved the highest accuracy of 90.16% compared to other ML algorithms implemented.
Supervised Machine Learning-Based Cardiovascular Disease Analysis and Prediction
Mathematical Problems in Engineering
Cardiovascular illness, often commonly known as heart disease, encompasses a variety of diseases that affect the heart and has been the leading cause of mortality globally in recent decades. It is associated with numerous risks for heart disease and a requirement of the moment to get accurate, trustworthy, and reasonable methods to establish an early diagnosis in order to accomplish early disease treatment. In the healthcare sector, data analysis is a widely utilized method for processing massive amounts of data. Researchers use a variety of statistical and machine learning methods to evaluate massive amounts of complicated medical data, assisting healthcare practitioners in predicting cardiac disease. This study covers many aspects of cardiac illness, as well as a model based on supervised learning techniques such as Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). It makes use of an existing dataset from the UCI Cleveland database of heart disease patients. Th...
The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction
Healthcare
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 93.4...
Cardiovascular Disease Prediction Using Machine Learning Approaches
International Journal of Innovative Research in Engineering and Management (IJIREM), 2023
Cardiovascular disease is a prominent contributor to global mortality. The timely identification and prognostication of cardiovascular disease can mitigate its incidence and diminish fatality ratios. The use of machine learning has emerged as a promising methodology for forecasting the likelihood of heart disease. The present study delves into the application of machine learning algorithms in the prediction of heart disease. In this study, a publicly accessible dataset on heart disease is utilized to assess the efficacy of various machine learning algorithms and determine the optimal models. The study involves a comparative analysis of various algorithms, namely Logistic Regression, Random Forest, Support Vector Machines, and Artificial Neural Networks, with respect to their accuracy and other performance metrics. The findings indicate that the Artificial Neural Network model yielded the highest level of performance, exhibiting an accuracy rate of 87.5%. The aforementioned showcases the prospective employment of machine learning in the domain of heart disease prognosis, thereby highlighting the exigency for additional inquiry in this field.