Comparative Analysis of Heart Disease Prediction Models: Unveiling the Most Accurate and Reliable Machine Learning Algorithm (original) (raw)

Performance Evaluation of Machine Learning Techniques (MLT) for Heart Disease Prediction

Computational and Mathematical Methods in Medicine

The leading cause of death worldwide today is heart disease (HD). The heart is recognised as the second-most significant organ behind the brain. A successful outcome of treatment can be improved by an early diagnosis which can significantly reduce the chance of death in health care. In this paper, we proposed a method to predict heart disease. We used various machine learning algorithms (MLA), namely, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), random forest (RF), and decision tree (DT). With the testing data set, we evaluated the model’s accuracy in heart disease prediction. When compared to the other five models, the random forest and k-nearest neighbor approaches perform better. With a 99.04% accuracy rate, the k-nearest neighbor algorithm and random forest provide the best match to the data as compared to other algorithms. Six feature selection algorithms were used for the performance evaluation matrix. MCC parameters for a...

An empirical study on machine learning algorithms for heart disease prediction

IAES International Journal of Artificial Intelligence, 2022

In recent years, machine learning is attaining higher precision and accuracy in clinical heart disease dataset classification. However, literature shows that the quality of heart disease feature used for the training model has a significant impact on the outcome of the predictive model. Thus, this study focuses on exploring the impact of the quality of heart disease features on the performance of the machine learning model on heart disease prediction by employing recursive feature elimination with cross-validation (RFECV). Furthermore, the study explores heart disease features with a significant effect on model output. The dataset for experimentation is obtained from the University of California Irvine (UCI) machine learning dataset. The experiment is implemented using a support vector machine (SVM), logistic regression (LR), decision tree (DT), and random forest (RF) are employed. The performance of the SVM, LR, DT, and RF models. The result appears to prove that the quality of the feature significantly affects the performance of the model. Overall, the experiment proves that RF outperforms as compared to other algorithms. In conclusion, the predictive accuracy of 99.7% is achieved with RF.

An Experimental Study of Various Machine Learning Approaches in Heart Disease Prediction

International Journal of Computer Applications, 2020

According to recent survey of WHO (World Health Organization) 17.9 million people die each year because of heart related diseases and it is increasing rapidly. With the increasing population and diseases, it has become challenging to diagnosis and treatment diseases at the right time. But there is a light of hope that recent advancements in technology have accelerated the public health sector by advanced functional biomedical solutions. This paper aims to analyze the various machine learning approaches namely Naïve Bayes (NB), Random Forest (RF) Classification, Decision tree (DT), Support Vector Machine (SVM) and Logistic Regression (LR) by employing a qualified dataset for heart disease prediction. This research finds the correlations between the various attributes that are suitable to predict the chances of a heart disease and compares the impact of Principle Component Analysis (PCA) on the accuracy of the above mentioned algorithms.

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.

Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction

IJSRP, 2021

Machine learning has become popular today as so many of its algorithms are now commonly used in different data science projects in various industries especially in the health care sector. It is imperative for researchers and medical professionals to be assisted by machine learning methods in early detection of diseases such as heart disease which is one major killer of humans in our world today. Machine learning algorithms are excellent at learning from data, and since healthcare providers generate huge amount of data on a daily basis, these algorithms can thrive in this field. In this research study, a comparative analytical approach was taken in the determination of which algorithm performs better under the given condition. Various experiments were carried out using cross validation of 5 and 10 folds, to ensure that models created can generalize well enough. This study makes use of data from University of California, Irvine (UCI) machine learning database containing 303 instances with 14 attributes. The collected data is scaled using Min-Max normalization technique. Different popular models are built using supervised machine learning classification algorithms on the scaled data such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and Gradient Boosting ensemble method. These algorithms are also evaluated using standard performance metrics such as precision, recall, and F1-score. From the experiments carried out, it can be concluded that SVM performs better as it out performs the other algorithms.

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.

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...

Cardiovascular Disease Prediction Using Machine Learning

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020

Heart-related diseases or Cardiovascular Diseases (CVDs) are the most common and main reasons for a huge number of deaths in the world, not only in India but in the whole world. So, there is a need for a reliable, accurate, and feasible system to diagnose such diseases in time for proper treatment. This research paper represents the various models based on such algorithms and techniques to analyze their performance. Such as Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, and ensemble models which are Supervised Learning algorithms. Using various important features that are necessary for the prediction of CVDs (like a person is having CVDs or not), which we will further discuss in this paper.

Heart Disease Prediction System Using Random Forest Technique

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Heart disease prediction and diagnosis have always been challenging tasks for medical experts. Hospitals and other medical facilities offer pricey procedures and treatments for cardiac diseases. As a result, being able to people all around the world can take the necessary actions to treat cardiac disease before it becomes severe if it is discovered in its early stages. The main causes of heart disease, a severe problem in recent years, are drinking alcohol, smoking cigarettes, and not exercising. A significant amount of data generated over time by the health care sector has allowed machine learning to offer efficient results in decision-making and prediction. We attempt to predict probable heart conditions in patients using machine learning approaches. In this project, we compare various classifiers, including decision trees, Naive Bayes, logistic regression, SVM, and random forests. We also suggest an ensemble classifier, which performs hybrid classification by combining the best features of both strong and weak classifiers and can handle large amounts of training and validation samples. We contrast already-in-use classifiers with others that have been proposed, such as Ada-boost and XGboost, which can offer higher accuracy. The main advantages of heart disease prediction using machine learning are that it manages the largest (enormous) quantity of data using the random forest algorithm and feature selection, as well as reducing the complexity of the doctors' time and being cost-and patient-friendly.

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.