Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction

2020 ◽  
Vol 11 (1) ◽  
pp. 339-346
Author(s):  
Isreal Ufumaka
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Comparative Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Prediction

scholarly journals Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction

2021 ◽  
Vol 40 ◽  
pp. 03007
Author(s):  
Ruby Hasan
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Comparative Analysis ◽  
Cardiovascular Diseases ◽  
Patient Monitoring ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
Advantages And Disadvantages

In the last few years, cardiovascular diseases have emerged as one of the most common causes of deaths worldwide. The lifestyle changes, eating habits, working cultures etc, has significantly contributed to this alarming issue across the globe including the developed, underdeveloped and developing nations. Early detection of the initial signs of cardiovascular diseases and the continuous medical supervision can help in reducing rising number of patients and eventually the mortality rate. However with limited medical facilities and specialist doctors, it is difficult to continuously monitor the patients and provide consultations. Technological interventions are required to facilitate the patient monitoring and treatment. The healthcare data generated through various medical procedures and continuous patient monitoring can be utilized to develop efficient prediction models for cardiovascular diseases. The early prognosis of cardiovascular illnesses can aid in making decisions on life-style changes in high hazard sufferers and in turn lessen the complications, which may be an outstanding milestone inside the field of medicine. This paper studies some of the most widely used machine learning algorithms for heart disease prediction by using the medical data and historical information. The various techniques are discussed and a comparative analysis of the same is presented. This report compares five common strategies for predicting the chance of heart attack that have been published in the literature. KNN, Decision Tree, Gaussian Naive Bayes, Logistic Regression, and Random Forest are some of the approaches used. Further, the paper also highlights the advantages and disadvantages of using the various techniques for developing the prediction models.

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

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.

scholarly journals HEART DISEASE PREDICTION SYSTEM USING MACHINE LEARNING ALGORITHM

2021 ◽  
Vol 1 (1) ◽  
pp. 146-176
Author(s):  
Israa Nadher ◽  
Mohammad Ayache ◽  
Hussein Kanaan
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Digital Data ◽  
Disease Prediction ◽  
Prediction System ◽  
Testing Dataset ◽  
Starting Point

Abstract—Information decision support systems are becomingmore in use as we are living in the era of digital data andrise of artificial intelligence. Heart disease as one of the mostknown and dangerous is getting very important attention, thisattention is translated into digital and prediction system thatdetects the presence of disease according to the available dataand information. Such systems faced a lot of problems since thefirst rise, but now with the deveolopment of machine learnigfield we are using them in developing new models to detect thepresence of this disease, in addition to algorithms data is veryimportant which also form the heart of the predicton systems,as we know prediction algorithms take decisions and thesedecisions must be based on facts, and these facts are extractedfrom data, as a result data is the starting point of every system.In this paper we propose a Heart Disease Prediction Systemusing Machine Learning Algorithms, in terms of data we usedCleveland dataset, this dataset is normalized then divided intothree scnearios in terms of traning and testing respectively,80%-20%, 50%-50%, 30%-70%. In each case of dataset ifit is normalized or not we will have these three scenarios.We used three machine learning algorithms for every scenarioof the mentioned before which are SVM, SMO and MLP, inthese algorithms we’ve used two different kernels to test theresults upon that. These two types of simulation are added tothe collection of scenarios mentioned above to become as thefollowing we have at the main level two types normalized andunnormalized dataset, then for each one we have three typesaccording to the amount of training and testing dataset, thenfor each of these scenarios we have two scenarios according tothe type of kernel to become 30 scenarios in total, our proposedsystem have shown a dominance in terms of accuracy over theother previous works.

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