Empirical Analysis of Machine Learning Algorithms on Imbalance Electrocardiogram Based Arrhythmia Dataset for Heart Disease Detection

2021 ◽  
Author(s):  
Shwet Ketu ◽  
Pramod Kumar Mishra
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Empirical Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Detection

scholarly journals Heart Disease Prediction using Supervised Machine Learning Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 148-151
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Disease Detection ◽  
Disease Prediction ◽  
Benchmark Datasets ◽  
Complicated Task

Generally, the most complicated task in the healthcare field is the diagnosis of the disease itself. The diagnosis phase in disease detection is usually the most time-consuming task and is prone to most of the errors. Such complications can be effectively handled if the disease detection process is well automated by incorporating effective machine learning algorithms trained with some benchmark datasets. It should also be noted that huge amounts of data that are acquired from Heart Specialization Hospitals are being wasted every year. In this paper, various classification algorithms have been used to train the machine to diagnose heart disease. By a comparative study of various learning models, we have identified the appropriate learning model for the heart disease dataset. Initially, the work will begin with an overview of various machine learning algorithms followed by the algorithmic comparison.

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 A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

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