scholarly journals Study of Machine Learning Algorithms for Prediction and Diagnosis of Cardiovascular Diseases : A Review

2020 ◽  
pp. 480-489
Author(s):  
Manoj Patil ◽  
Harsh Mathur
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Early Stage ◽  
Correct Diagnosis ◽  
Machine Learning Algorithms ◽  
Knowledge Discovery In Databases ◽  
Disease Prediction ◽  
Using Data ◽  
Modern Era

We are living in a post modern era and there are tremendous changes happening to our daily life which make an impact on our health positively and negatively. As a result of these changes various kind of diseases are enormously increased. In the medical field, the diagnosis of cardiovascular disease is the most difficult task. The diagnosis of cardiovascular disease is difficult as a decision relied on grouping of large clinical and pathological data. Due to this complication, the interest increased in a significant amount between the researchers and clinical professionals about the efficient and accurate heart disease prediction. In case of heart disease, the correct diagnosis in early stage is important as time is the very important factor. Heart disease is the principal source of deaths widespread, and the prediction of Heart Disease is significant at an untimely phase. Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has provided the greatest support for predicting disease with correct case of training and testing. This research paper intends to provide a survey of techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research particularly in Cardiovascular Disease Prediction.

An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Fathima Aliyar Vellameeran ◽  
Thomas Brindha
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Research Papers ◽  
Using Data ◽  
Intelligent Methods

Abstract Objectives To make a clear literature review on state-of-the-art heart disease prediction models. Methods It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

scholarly journals Predicting Heart Disease at Early Stages using Machine Learning: A Survey

2021 ◽  
Vol 23 (09) ◽  
pp. 1178-1181
Author(s):  
Mr. Sumit Hawal ◽  
◽  
Dr. Sandeep Dwarkanath Pande ◽  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Primary Objective ◽  
Accurate Diagnosis ◽  
Disease Prediction ◽  
Medical Field ◽  
Critical Machine

Cardiovascular disease diagnosis is the most difficult task in medicine. The diagnosis of heart disease is complicated because it requires the grouping of massive volumes of clinical and pathological data. As a result of this dilemma, researchers and clinical professionals have developed a strong interest in the efficient and exact prediction of heart disease. When it comes to heart disease, it is critical to obtain an accurate diagnosis at an early stage because time is of the essence. Heart disease is the largest cause of death worldwide, and early detection of heart disease is critical. Machine learning has evolved as one of the most progressive, dependable, and supportive tools in the medical field in recent years, providing the greatest assistance for disease prediction when properly trained and tested. The primary objective of this research is to evaluate several algorithms for heart disease prediction.

A Comparative Study of Two Machine Learning Algorithms for Heart Disease Prediction System

2021 ◽  
Author(s):  
Wan Adlina Husna Wan Azizan ◽  
A'zraa Afhzan Ab Rahim ◽  
Siti Lailatul Mohd Hassan ◽  
Ili Shairah Abdul Halim ◽  
Noor Ezan Abdullah
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
Prediction System

Heat Disease Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 846-852
Author(s):  
Prof. Dr. R. Sandhiya
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Assessment Process ◽  
Disease Prediction ◽  
Test Results ◽  
Medical Field ◽  
Modern Age ◽  
Linear Svm

In recent times, the diagnosis of heart disease has become a very critical task in the medical field. In the modern age, one person dies every minute due to heart disease. Data science has an important role in processing big amounts of data in the field of health sciences. Since the diagnosis of heart disease is a complex task, the assessment process should be automated to avoid the risks associated with it and alert the patient in advance. This paper uses the heart disease dataset available in the UCI Machine Learning Repository. The proposed work assesses the risk of heart disease in a patient by applying various data mining methods such as Naive Bayes, Decision Tree, KNN, Linear SVM, RBF SVM, Gaussian Process, Neural Network, Adabost, QDA and Random Forest. This paper provides a comparative study by analyzing the performance of various machine learning algorithms. Test results confirm that the KNN algorithm achieved the highest 97% accuracy compared to other implemented ML algorithms.

scholarly journals Heart Disease Prediction Using Machine Learning Algorithms

2020 ◽  
pp. 137-149
Author(s):  
Abhay Agrahary
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Accurate Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Huge Amount ◽  
Learning Techniques ◽  
Comparative Results

Heart disease is one of the most fatal problems in the whole world, which cannot be seen with a naked eye and comes instantly when its limitations are reached. Therefore, it needs accurate diagnosis at accurate time. Health care industry produced huge amount of data every day related to patients and diseases. However, this data is not used efficiently by the researchers and practitioners. Today healthcare industry is rich in data however poor in knowledge. There are various data mining and machine learning techniques and tools available to extract effective knowledge from databases and to use this knowledge for more accurate diagnosis and decision making. Increasing research on heart disease predicting systems, it become significant to summarize the completely incomplete research on it. The main objective of this research paper is to summarize the recent research with comparative results that has been done on heart disease prediction and also make analytical conclusions. From the study, it is observed Naive Bayes with Genetic algorithm; Decision Trees and Artificial Neural Networks techniques improve the accuracy of the heart disease prediction system in different scenarios. In this paper commonly used data mining and machine learning techniques and their complexities are summarized.

scholarly journals Heart disease prediction using Advanced Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 2160-2163
Author(s):  
Minal Shahakar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Web Application ◽  
Intelligent System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
End User ◽  
Data Mining Techniques

It might have happened so many times that you or someone yours need doctors help immediately, but they are not available due to some reason. The Heart Disease Prediction application is an end user support to the online. Here, we propose a web application that allows users to get instant guidance on their heart disease through an intelligent system online. The application is fed with various details and the heart disease associated with those details. The applications allows user to share their heart related issues. It then processes user specific details to check for various illnesses that could be associated with it. Here we use some intelligent data mining techniques to the most accurate that could be associated with patient‟s details. Based on result, system automatically shows the result specific doctors for further treatment and the system allows user to view doctor‟s details.

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