A Comparative Study on Machine Learning and Artificial Neural Networking Algorithms

A Comparative Study on Machine Learning and Artificial Neural Networking Algorithms

Table of Contents


The diagnosis of heart disease by the classical medical approach takes a huge amount of time. Besides blood tests and X-ray, this approach includes multiple tests like MRI, Echocardiogram, and whose results are prone to misdiagnosis. Our proposed model can predict whether a patient with given health parameters and certain test results is affected by heart disease. The proposed model uses an AI approach with several ML algorithms like KNN, SVM, Decision Tree, Random forest classifiers, and also with deep neural networks. This prediction is done based on the historical data collected from different medical Institutes in Central Europe.


  • Author Keywords

    • KNN Classifier,
    • Decision Tree,
    • Support Vector Machine,
    • Random Forest,
    • Artificial Neural Network
  • IEEE Keywords

    • Diseases,
    • Heart,
    • Data mining ,
    • Prediction algorithms,
    • Support vector machines,
    • Data models,
    • Machine learning algorithms



Diabetes, Blood Pressure, Cholesterol level, and Pulse rate are the several risk factors used for the identification of heart disease and it causes premature death.Heart Failure is the major cause of heart disease. Sweating, high level of fatigue, fast heartbeat, breathing, and chest pain are the symptoms of heart disease and these symptoms match for a person older than 13 years. The algorithms are classified as K Nearest Neighbour (KNN), Decision trees, Random forest, Support Vector Machine (SVM), and Backpropagation. In this method, 13 parameters. The dataset consists of age, sex, and medical reports such as blood glucose level, type of the pain in the chest area, heart rate analysis, etc,.These parameters figures out the predict the nature of the heart disease with maximum accuracy.


This paper is proposed for Heart disease prediction using several Machine Learning algorithms and neural networks. This model supports the suitable treatment developed using certain characteristics. All the models are trained and tested with proper data processing and data analyzing. The final comparisons prove to be more efficient for the Back Propagation Algorithm of Artificial Neural Networks, with 89% accuracy and speed.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.

The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.

FULL Paper PDF file:

A Comparative Study on Machine Learning and Artificial Neural Networking Algorithms



R. Udaiyakumar, N. Vijayalakshmi, M. Prashanthram, and S. Jayaprakash,




A Comparative Study on Machine Learning and Artificial Neural Networking Algorithms

Publish in

2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 516-517, 



PDF reference and original file: Click here


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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.