scholarly journals Identification and Implementation Perspective on the Stratification Algorithms in the Prognostication of Heart Disease using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 1223-1225
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Heart Diseases ◽  
Classification Algorithm ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Combination Of Classifiers ◽  
Great Idea

Analysis of patient’s data is always a great idea to get accurate results on using classifiers. A combination of classifiers would give an accurate result than using a single classifier because one single classifier does not give accurate results but always appropriate ones. The aim is to predict the outcome feature of the data set. The “outcome” can contain only two values that is 0 and 1. 0 means patient doesn’t have heart disease and 1 means patient have heart diseases. So, there is a need to build a classification algorithm that can predict the Outcome feature of the test dataset with good accuracy. For this understanding the data is important, and then various classification algorithm can be tested. Then the best model can be selected which gives highest accuracy among all. The built model can then be given to the software developer for building the end user application using the selected machine learning model that will be able to predict the heart disease in a patient.

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.

Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus

2019 ◽  
pp. 198-217
Author(s):  
Angela Pimentel ◽  
Hugo Gamboa ◽  
Isa Maria Almeida ◽  
Pedro Matos ◽  
Rogério T. Ribeiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Health Authorities ◽  
Learning Techniques ◽  
Alternating Decision Tree

Heart diseases and stroke are the number one cause of death and disability among people with type 2 diabetes (T2D). Clinicians and health authorities for many years have expressed interest in identifying individuals at increased risk of coronary heart disease (CHD). Our main objective is to develop a prognostic workflow of CHD in T2D patients using a Holter dataset. This workflow development will be based on machine learning techniques by testing a variety of classifiers and subsequent selection of the best performing system. It will also assess the impact of feature selection and bootstrapping techniques over these systems. Among a variety of classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Alternating Decision Tree (ADT), Random Tree (RT) and K-Nearest Neighbour (KNN), the best performing classifier is NB. We achieved an area under receiver operating characteristics curve (AUC) of 68,06% and 74,33% for a prognosis of 3 and 4 years, respectively.

scholarly journals A new smart healthcare framework for real-time heart disease detection based on deep and machine learning

2021 ◽  
Vol 7 ◽  
pp. e646
Author(s):  
Haitham Elwahsh ◽  
Engy El-shafeiy ◽  
Saad Alanazi ◽  
Medhat A. Tawfeek
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Real Time ◽  
Heart Diseases ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Rate Condition ◽  
Smart Healthcare ◽  
Learning Techniques

Cardiovascular diseases (CVDs) are the most critical heart diseases. Accurate analytics for real-time heart disease is significant. This paper sought to develop a smart healthcare framework (SHDML) by using deep and machine learning techniques based on optimization stochastic gradient descent (SGD) to predict the presence of heart disease. The SHDML framework consists of two stage, the first stage of SHDML is able to monitor the heart beat rate condition of a patient. The SHDML framework to monitor patients in real-time has been developed using an ATmega32 Microcontroller to determine heartbeat rate per minute pulse rate sensors. The developed SHDML framework is able to broadcast the acquired sensor data to a Firebase Cloud database every 20 seconds. The smart application is infectious in regard to displaying the sensor data. The second stage of SHDML has been used in medical decision support systems to predict and diagnose heart diseases. Deep or machine learning techniques were ported to the smart application to analyze user data and predict CVDs in real-time. Two different methods of deep and machine learning techniques were checked for their performances. The deep and machine learning techniques were trained and tested using widely used open-access dataset. The proposed SHDML framework had very good performance with an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87.

scholarly journals Heart Disease Prediction using Machine Learning and Data Mining

2020 ◽  
Vol 9 (1) ◽  
pp. 21-219
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
K Nearest Neighbors ◽  
Learning Techniques ◽  
Rapid Pace ◽  
Novel Method ◽  
Unbalanced Diet ◽  
Major Factors

Weighing only 300 grams, Heart is declining the mortality rate at a rapid pace from decades. The major factors that contribute to it are smoking, drinking, unbalanced diet, and many more. Even with these more technical advancements the analysis of the clinical data is a critical challenge. With the use of Machine Learning techniques, it is possible to analyse the data and interpret the cause that lead to heart diseases such as Coronary Heart Disease, Arrhythmia, and Dilated Cardiomyopathy. Many researchers are developing IoT enabled hardware to predict these diseases using various ML and DM techniques. In this study, we propose a novel method to determine the disease using Cleveland Heart Disease Dataset by combining the computational power of various ML and DM algorithms and concluded that among all the algorithms, K-Nearest Neighbors gives the highest accuracy of 87%. Along with this, a web app is developed using flask in python with which the user can enter the attributes and predict the heart disease.

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. 

Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus

2017 ◽  
pp. 89-112
Author(s):  
Angela Pimentel ◽  
Hugo Gamboa ◽  
Isa Maria Almeida ◽  
Pedro Matos ◽  
Rogério T. Ribeiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Health Authorities ◽  
Learning Techniques ◽  
Alternating Decision Tree

Heart diseases and stroke are the number one cause of death and disability among people with type 2 diabetes (T2D). Clinicians and health authorities for many years have expressed interest in identifying individuals at increased risk of coronary heart disease (CHD). Our main objective is to develop a prognostic workflow of CHD in T2D patients using a Holter dataset.. This workflow development will be based on machine learning techniques by testing a variety of classifiers and subsequent selection of the best performing system. It will also assess the impact of feature selection and bootstrapping techniques over these systems. Among a variety of classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Alternating Decision Tree (ADT), Random Tree (RT) and K-Nearest Neighbour (KNN), the best performing classifier is NB. We achieved an area under receiver operating characteristics curve (AUC) of 68,06% and 74,33% for a prognosis of 3 and 4 years, respectively.

scholarly journals Prediction of Heart Disease Using Machine Learning

2021 ◽  
pp. 213-217
Author(s):  
Prasanna M ◽  
Shrijith Shetty P ◽  
Mamatha K
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Way Of Life ◽  
Learning Techniques ◽  
Knn Classification ◽  
Causes Of Mortality ◽  
Genetic Problem ◽  
Day By Day

Heart disease is one of the most significant causes of mortality in the world today. Prediction of Heart disease is a critical challenge in the area of clinical data analysis. Machine learning has been shown to be effective in assisting in making decision and predictions from large quantity of data produced by the health industry. Several types of heart diseases are expanding day by day because of way of life, genetic problem, blood pressure, cholesterol level, pulse rate etc. So the diagnose of disease plays important role for the prevention of heart related problems. Researchers received different methods to analyze it. This system aims at Predicting heart disease with various machine learning techniques and increasing the accuracy of the system. Classification approach consists of two algorithms such as KNN classification algorithm and Decision tree algorithm. The result of classification shows 86% accuracy by using n number of neighbors in this approach.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

Borderline and Depression: A Thin EEG Line

2021 ◽  
pp. 155005942110608
Author(s):  
Jakša Vukojević ◽  
Damir Mulc ◽  
Ivana Kinder ◽  
Eda Jovičić ◽  
Krešimir Friganović ◽  
...  
Keyword(s):  
Machine Learning ◽  
Borderline Personality ◽  
Machine Learning Techniques ◽  
Major Depressive ◽  
Everyday Clinical Practice ◽  
Data Set ◽  
Learning Techniques ◽  
Eeg Recordings ◽  
The Given ◽  
Close Interrelationship

In everyday clinical practice, there is an ongoing debate about the nature of major depressive disorder (MDD) in patients with borderline personality disorder (BPD). The underlying research does not give us a clear distinction between those 2 entities, although depression is among the most frequent comorbid diagnosis in borderline personality patients. The notion that depression can be a distinct disorder but also a symptom in other psychopathologies led our team to try and delineate those 2 entities using 146 EEG recordings and machine learning. The utilized algorithms, developed solely for this purpose, could not differentiate those 2 entities, meaning that patients suffering from MDD did not have significantly different EEG in terms of patients diagnosed with MDD and BPD respecting the given data and methods used. By increasing the data set and the spatiotemporal specificity, one could have a more sensitive diagnostic approach when using EEG recordings. To our knowledge, this is the first study that used EEG recordings and advanced machine learning techniques and further confirmed the close interrelationship between those 2 entities.

Sign in / Sign up

Export Citation Format

Share Document