Application of Machine Learning for Cardiovascular Disease Risk Prediction (original) (raw)

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.

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.

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.

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...

Prediction of Cardiovascular Disease on Different Parameters Using Machine Learning

International Journal of Scientific Research in Science, Engineering and Technology, 2021

The most common serious diseases affecting human health are cardiovascular diseases (CVDs). Early diagnosis can prevent or mitigate CVDs, which can reduce the rate of death. It's a promising approach to identify risk factors using machine learning models. We wish to propose a model with different methods to effectively predict heart disease. We have employed effective data collection, data pre-processing and data transformation methods for the precise information of our training model to make our proposed model a success. A combined dataset has been used (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). The appropriate function is selected using AASSO (Advanced Absolute Shrinkage and Selection Operator techniques) and AASSO techniques. Appropriate features are selected. New hybrids are developed with integration of the traditional bagging and boosting methods, such as Decision Tree Bagger Method (DTBM), the Random Forest Bagging Method (RFBM), the K-Nearest Neighbour Bagging method (KNNBM), the AdaBoost Boosting Method (ABBM), and the GBBM. Our machine learning algorithms, along with Negative Predictive Value (NGR, false positive rates), and false negative flow rates, also were implemented to calculate accuracy of our model, sensitivity (SEN), error rate, accuracy of the model (FRE) and the F1 score (F1) (FNR). The results are shown for comparisons separately. Based on the result analysis, our proposed model produced the highest precision, Accuracy using RFBM and relief selection methods (99.05 percent).

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Computational and Mathematical Methods in Medicine

Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of t...

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.

Use of Machine Learning Techniques in the Prediction of Heart Disease

Machine learning is a fiction that belongs to the computer science realm; in reality, it is an interdisciplinary subject with applications in every field. Machine learning techniques are being used in signal processing, picture and speech recognition, electronic design automation, and self-driving cars, to name a few. The goal of this paper is to determine the method that provides the highest level of accuracy about heart disease. The rationale for this research is that if we can anticipate heart disease as early as feasible, we can lower the risk and begin treatment as soon as possible. We can also shorten the time it takes to diagnose, and we can handle enormous amounts of medical data with ease using machine learning techniques.We chose Python to implement the project since it is simple to learn, understand, and implement.Python is free and open source, and it comes with a number of machine learning libraries. On the training dataset, we trained the model using several methods such as Logistic Regression, k Nearest Neighbors(kNN), Decision Trees, and Random Forest in order to predict heart disease, and we tested the model's accuracy using the testing data set. The performance of the Random Forest algorithm is found good compared to the remaining three algorithms. Random forest algorithm best fits the data with an accuracy of 88.16%.

Cardiovascular Disease Prediction Model using Machine Learning Algorithms

international journal for research in applied science and engineering technology ijraset, 2020

A general term for conditions affecting the heart or blood vessels is called as Cardiovascular disease (CVD). It is commonly associated with an increased risk of blood clots and build-up of fatty deposits inside the arteries (atherosclerosis). Sometimes, it can also be associated with damage to arteries in organs such as the brain, kidneys, heart and eyes. CVD is the reason for the highest number of deaths globally and the major cause of death annually. Most cardiovascular diseases can often be prevented by leading a healthy lifestyle and addressing behavioural risk factors such as unhealthy diet and obesity, tobacco use, harmful use of alcohol and physical inactivity using population-wide strategies. Machine Learning can play an important role in predicting cardiovascular disease and such information, if predicted well in advance can provide significant insights to doctors who can then adapt their treatment and diagnosis for each patient accordingly. In the proposed research method, firstly the attributes are selected from the dataset, then data pre-processing takes place which uses techniques such as removal of noisy data, removal of missing data, filling default values if applicable, classification of attributes for prediction and decision making at different levels. Classification, accuracy, sensitivity and specificity analysis is done to obtain the performance of the diagnosis model. A prediction model which predicts whether a person has a heart disease or not and hence provide diagnosis or discussion on the results is proposed. This is accomplished by applying rules to the individual results of classification algorithms such as Gradient Boosting Classifier, Random Forest Classifier, Support Vector Machine, Extremely Randomized Trees Classifier (Extra Trees Classifier), Logistic Regression and Multi-Layer Perceptron (MLP) Classifier obtained on the dataset.

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...