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.

scholarly journals Effective Cardiovascular Disease Prediction using Hybrid Machine Learning Techniques

2020 ◽  
Vol 9 (4) ◽  
pp. 1555-1558
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Accurate Diagnosis ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Naked Eye ◽  
The People ◽  
Learning Techniques ◽  
Novel Method

In today’s era deaths due to heart disease has become a major issue approximately one person dies per minute due to heart disease. This is considering both male and female category and this ratio may vary according to the region also this ratio is considered for the people of different age groups.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.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.In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. We produce an enhanced performance level with an accuracy through the prediction model for heart disease with the proposed method hybrid random forest with a linear model.In this paper commonly used machine learning techniques and their complexities are summarized.

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.

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.

Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 373-381
Author(s):  
Shiva Shanta Mani B. ◽  
Manikandan V. M.
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Early Stage ◽  
Heart Diseases ◽  
Age Groups ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Health Issues ◽  
Decision Tree Classifier ◽  
Knn Classifier

Heart disease is one of the most common and serious health issues in all the age groups. The food habits, mental stress, smoking, etc. are a few reasons for heart diseases. Diagnosing heart issues at an early stage is very much important to take proper treatment. The treatment of heart disease at the later stage is very expensive and risky. In this chapter, the authors discuss machine learning approaches to predict heart disease from a set of health parameters collected from a person. The heart disease dataset from the UCI machine learning repository is used for the study. This chapter discusses the heart disease prediction capability of four well-known machine learning approaches: naive Bayes classifier, KNN classifier, decision tree classifier, random forest classifier.

Heart Disease Prediction System Using Ensemble of Machine Learning Algorithms

2019 ◽  
Vol 13 ◽  
Author(s):  
Nandhini Abirami R. ◽  
Durai Raj Vincent
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithms ◽  
Medical Intervention ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
Prediction System ◽  
Random Forest Algorithm ◽  
Medical Field

Background: Diagnosing diseases is an intricate job in medical field. Machine learning when applied to health care is capable of early detection of disease which would aid to provide early medical intervention. In heart disease prediction, machine learning techniques have played a significant role. Analysis of disease has become vital in health care sectors. The massive data collected by healthcare sectors are preprocessed and analyzed to discover the underlying information in the data for effective decision making and to provide proper medical intervention. The success of machine learning in medical industry is its capability in analyzing the huge amount of data gathered by the health sector and its effectiveness in decision making. Since medical field involves too many manual processes it has become necessary to automate these procedures. Remarkable advancements in electronic medical records have made it possible. Diagnosing diseases is an intricate job in medical field. Objective: The objective of this research is to design a robust machine learning algorithm to predict heart disease. The prediction of heart disease is performed using Ensemble of machine learning algorithms. This is to boost the accuracy achieved by individual machine learning algorithms. Method: Heart Disease Prediction System is developed where the user can input the patient details and the prediction for the particular patient is made using the model developed. The model will predict the output to be either normal or risky. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes classifier are used as base learners. These algorithms are combined using random forest as the meta classifier. Results: The predictions of classifier are combined using random forest algorithm. The accuracy is lifted from 85.53% to 87.64% which is an impressive improvement on accuracy. Conclusion: Various techniques were adopted to preprocess the data to suite the requirement of analysis. Feature selections were made to optimize the performance of machine learning algorithms. Ensemble prediction gave better accuracy when combined using Random forest algorithm as combiner. Better feature selection techniques can be applied to further improve the accuracy.

scholarly journals Heart Disease Prediction Model based Ongradient Boosting Tree (GBT) Classification Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 41-51
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Performance Measures ◽  
Death Rate ◽  
Gradient Boosting ◽  
Disease Prediction ◽  
Data Complexity ◽  
Medical Field ◽  
The World ◽  
Experimental Values

Recently, Heart disease (HD) is the main cause of increasing death rate all over the world. Data classification is a crucial task in the medical field which assists the physicians to predict the diseases. Recently, machine learning (ML) algorithms have been employed to classify the data in the medical field. The data complexity and quantity needs to be examined and managed to transform the efficient and accurate HD diagnosis. In this paper, a gradient boosting tree (GBT) based classifier or gradient boosting classifier (GBC) model to predict the HD efficiently. Besides, a set of extensive experiments were carried out using Staglog and Cleveland heart disease dataset. The experimental values ensured the superiority of the GBT classifier based on several performance measures.

scholarly journals Different Machine Learning Models Based Heart Disease Prediction

2020 ◽  
Vol 8 (6) ◽  
pp. 544-548
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Early Detection ◽  
Automatic System ◽  
Early Stage ◽  
Medical Data ◽  
Disease Prediction ◽  
Learning Models ◽  
Related Disease ◽  
Machine Learning Models

Heart related disease is one of the crucial reasons for high amount of people’s death in the whole countries and it’s considered as life forbidding disorder, in addition to that this effect takes place in whole earth. Heart disease will affect the early stage of age peoples also. Thus, heart related disease creates the more challenges to people living and identify the causes and detection step is more important in nowadays. So, we need to develop of automatic system with more accurate and reliable for early detection of heart disease. For this reason, various machine learning models are developed to predict heart related disease; different medical data package is processed to automatic analysis with get more accuracy. In this paper, we discuss the available machine learning models such as KNN, SVM, DT and RF algorithms for prognosis of heart disease with high certitude, precision and recall.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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