scholarly journals Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 267-276
Author(s):  
Baban. U. Rindhe ◽  
Nikita Ahire ◽  
Rupali Patil ◽  
Shweta Gagare ◽  
Manisha Darade
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Whole Body ◽  
Support Vector ◽  
Learning Techniques

Heart-related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need fora reliable, accurate, and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart-related diseases. Heart is the next major organ comparing to the brain which has more priority in the Human body. It pumps the blood and supplies it to all organs of the whole body. Prediction of occurrences of heart diseases in the medical field is significant work. Data analytics is useful for prediction from more information and it helps the medical center to predict various diseases. A huge amount of patient-related data is maintained on monthly basis. The stored data can be useful for the source of predicting the occurrence of future diseases. Some of the data mining and machine learning techniques are used to predict heart diseases, such as Artificial Neural Network (ANN), Random Forest,and Support Vector Machine (SVM).Prediction and diagnosingof heart disease become a challenging factor faced by doctors and hospitals both in India and abroad. To reduce the large scale of deaths from heart diseases, a quick and efficient detection technique is to be discovered. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop software with thehelp of machine learning algorithms which can help doctors to decide both prediction and diagnosing of heart disease. The main objective of this research project is to predict the heart disease of a patient using machine learning algorithms.

scholarly journals A Novel Approach for Prediction of Heart Disease: Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 108
Author(s):  
V Srinivas ◽  
K Aditya ◽  
G Prasanth ◽  
R G.Babukarthik ◽  
S Satheeshkumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Optimization Algorithm ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Computation Time ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Backpropagation Algorithm ◽  
Learning Techniques

Heart disease and machine learning are the two different words where one is related to medical field and another one to artificial intelligence. In medical filed most of them are facing the problems with the heart disease and machine learning is developing area in computer science. Heart disease is general called cardiac disease where it gives the more data or information, it is to be collected to give the reports for the patients and the machine learning also requires the data for predicting and to solve the problems. Machine learning techniques are used in prediction of heart diseases where it gives the faster prediction with less computation time and better accuracy to progress their health. Heart disease prediction requires lot of data for predicting and in cloud computing also we have more data and the data available in cloud it is difficult to analyze. So we use machine learning algorithms or techniques to predict the heart disease and the in the similar way we can apply these algorithms or techniques to predict or analyze the data that is available in cloud. In this paper we are going to use machine learning algorithms called Backpropagation Algorithm and later we use optimization algorithm later. Backpropagation algorithm deals with the artificial neural networks. Backpropagation is a method used to calculate the error contribution of each neuron after a batch of data (in image recognition, multiple images) is processed. This is used by an enveloping optimization algorithm to adjust the weight of each neuron, completing the learning process for that case. Machine learning algorithms and techniques are used for recognize the intensity of risk issues in humans and it helps the patients to take safety measures in well advances to save the patient’s life. 

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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 Framework for Providing Security in Private Cloud using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 7641-7645
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithms ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cyber Attack ◽  
Learning Techniques

The advancement in cyber-attack technologies have ushered in various new attacks which are difficult to detect using traditional intrusion detection systems (IDS).Existing IDS are trained to detect known patterns because of which newer attacks bypass the current IDS and go undetected. In this paper, a two level framework is proposed which can be used to detect unknown new attacks using machine learning techniques. In the first level the known types of classes for attacks are determined using supervised machine learning algorithms such as Support Vector Machine (SVM) and Neural networks (NN). The second level uses unsupervised machine learning algorithms such as K-means. The experimentation is carried out with four models with NSL- KDD dataset in Openstack cloud environment. The Model with Support Vector Machine for supervised machine learning, Gradual Feature Reduction (GFR) for feature selection and K-means for unsupervised algorithm provided the optimum efficiency of 94.56 %.

Prediction Models for Healthcare Using Machine Learning

2021 ◽  
pp. 70-91
Author(s):  
Ayushe Gangal ◽  
Peeyush Kumar ◽  
Sunita Kumari ◽  
Anu Saini
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Brain Diseases ◽  
Health Issues ◽  
Industry Study ◽  
Learning Techniques

Healthcare is always a sensitive issue for all of us, and it will always remain. Predicting various types of health issues in advance can lead us to a better life. Various types of health problems are there like cancer, heart diseases, diabetes, arthritis, pneumonia, lungs disease, liver disease, and brain disease, which all are at high risk. To reduce the risk of health issues, some suitable models are needed for prediction. Thus, it became as a motivational factor for the authors to survey the existing literature on this topic thoroughly and have consequently to identify suitable machine learning techniques so that improvement can be possible while selecting a prediction model. In this chapter, concept of survey is used to provide the prediction models for healthcare issues along with the challenges associated with each model. This chapter will broadly cover the following: machine learning algorithms used in health industry, study various prediction models for Cancer, Heart diseases, Diabetes and Brain diseases, comparative study of various machine learning algorithms used for prediction.

scholarly journals Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 387
Author(s):  
Nahina Islam ◽  
Md Mamunur Rashid ◽  
Santoso Wibowo ◽  
Cheng-Yuan Xu ◽  
Ahsan Morshed ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Weed Detection ◽  
Learning Techniques ◽  
Positive Rate ◽  
Uav Images

This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.

scholarly journals Heart Disease Prediction using Neural Network

2021 ◽  
Vol 9 (VI) ◽  
pp. 1907-1910
Author(s):  
Ganesh Nanekar
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Whole Body ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Monthly Basis ◽  
Related Data ◽  
Learning Techniques

Heart is the next major organ comparing to brain which has more priority in Human body. It pumps the blood and supplies to all organs of the whole body. Prediction of occurrences of heart diseases in medical field is significant work. Data analytics is useful for prediction from more information and it helps medical Centre to predict of various disease. Huge amount of patient related data is maintained on monthly basis. The stored data can be useful for source of predicting the occurrence of future disease. Some of the data mining and machine learning techniques are used to predict the heart disease, such as Decision tree, Fuzzy Logic, K-Nearest Neighbor (KNN), Naïve Bayes and Support Vector Machine (SVM). This paper provides an insight of the existing algorithms and implements hybrid algorithms to improve accuracy significantly.

scholarly journals Prediction of Heart Disease Using Machine Learning Algorithms

2021 ◽  
Vol 36 (1) ◽  
pp. 260-264
Author(s):  
S. Ravi ◽  
Dr.M. Sambath ◽  
Dr.J. Thangakumar ◽  
Danam Kumar ◽  
Gorantla Naveen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithms ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Hospital Treatment ◽  
Learning Techniques ◽  
Automate Machine ◽  
Disease Hospital

As big data becomes more prevalent in the healthcare and medical sectors, accurate medical data collection benefits early diagnosis of heart disease, hospital treatment, and government resources. However, where medical data quality is lacking, understanding accuracy suffers. Consequently, some field diseases have unique features in different regions, which can make illness more difficult. It is now more hard to predict outbreaks. We automate machine learning algorithms for efficient epidemic detection in bacterial infection population in this paper. We put the modified forecasts to the test using securely and efficiently datasets. areas of the region to improve the situation of lost data, we use a predictive modeling approach to restore inaccurate value. Focused upon its patient's signs, a heart attack is suspected. Models were built using machine learning techniques. As a consequence, the accuracy is pinpoint accurate. The Flask web interface is used to build the Application. In this research, we shall conduct experiments using machine learning methods.

scholarly journals Heart Disease Prediction using Machine Learning Techniques

2020 ◽  
Vol 9 (5) ◽  
pp. 1456-1460
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Health Care ◽  
Heart Disease ◽  
Early Phase ◽  
Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Learning Techniques

Recent advancement of technology allows the automation of things to be done using machine learning techniques. These machine learning techniques can also be used for detecting or predicting the heart disease in the early phase. The health care industry produces a huge amount of data which is in unstructured manner that cannot be understood by a machine. Due to development of modern technology, health care industries also managing the data in a structured manner which can be understood by machine learning technology. In this environment if we use machine learning algorithms for prediction of heart disease, then there is a chance to detect the heart disease status in the early phase and to alert patient to get a better treatment to cure that disease. This paper implements seven supervised learning algorithms which are KNN, Decision Tree, Naive Bayes, Logistic Regression, Random Forest, Support Vector Machine and Neural Networks for heart disease prediction. This paper generates algorithm performance metrics like Accuracy, Precision, Recall, F-score and ROC values for how the system was predicting accurately. In this paper among those seven algorithms, Neural Networks gave best accuracy as 92.30% and this system provides experimental results for how the model is accurate for heart disease prediction.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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