Heart Attack Probability Analysis Using Machine Learning

2021 ◽  
Author(s):  
Annapurna Anant Shanbhag ◽  
Chinmai Shetty ◽  
Alaka Ananth ◽  
Anjali Shridhar Shetty ◽  
K Kavanashree Nayak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Attack ◽  
Probability Analysis ◽  
Attack Probability

scholarly journals Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 105
Author(s):  
Khaleel Husain ◽  
Mohd Soperi Mohd Zahid ◽  
Shahab Ul Hassan ◽  
Sumayyah Hasbullah ◽  
Satria Mandala
Keyword(s):  
Machine Learning ◽  
Heart Attack ◽  
Privacy Preservation ◽  
Causes Of Death ◽  
Clinical Settings ◽  
Data Format ◽  
Medical Institutions ◽  
Comprehensive Survey ◽  
Electrocardiogram Ecg ◽  
The Relationship

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.

scholarly journals An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Muhammad Waqar ◽  
Hassan Dawood ◽  
Hussain Dawood ◽  
Nadeem Majeed ◽  
Ameen Banjar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Heart Attack ◽  
High Reliability ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Unequal Distribution ◽  
The Given

Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

scholarly journals Machine Learning Models Predicting the Survival of Abrupt Heart Attack in Cardiac Sarcoidosis Disease using the Wearable Device

2020 ◽  
Vol 9 (5) ◽  
pp. 9-13
Keyword(s):  
Machine Learning ◽  
Ventricular Tachycardia ◽  
Ventricular Fibrillation ◽  
Heart Attack ◽  
Cardiac Sarcoidosis ◽  
Wearable Device ◽  
Cardioverter Defibrillator ◽  
Wearable Cardioverter Defibrillator ◽  
Positive Rate ◽  
Machine Learning Models

Wearable technology has countless prospects of remodelling healthcare establishment and also medical education. Cardiac sarcoidosis disease (CS) is a sporadic illness in which white blood cells (WBC) clusters known as granulomas, form as heart tissue. Cardiac sarcoidosis disease (CS) Patients are at high threat of ventricular tachycardia or ventricular fibrillation (VT/VF). Wearable cardioverter defibrillator device is introduced which helps to alleviate the abrupt heart attack risk amid patients of cardiac sarcoidosis. A reflective evaluation of the commercial record acknowledged patients of cardiac sarcoidosis disease who sported the wearable cardioverter defibrillator (WCD). ML models are applied to get accurate predictions to motivate WCD wear ability. The wearable device cardioverter defibrillator (WCD) was worn by forty six patients of cardiac sarcoidosis disease in which 22(48%) female, male 24 (52%). The wearable cardioverter defibrillator (WCD) was sported hours about 23.6 median daily. Nearby eleven ventricular tachycardia or ventricular fibrillation (VT/VF) incidents occur in ten patients (22%). Ventricular tachycardia or ventricular fibrillation (VT/VF) happened over a series of (1-79) days, median of twenty-four days. 1st- heart attack success for ventricular tachycardia or ventricular fibrillation (VT/VF) conversion was hundred percent. Survival of Patient in twenty four hours after treatment of attack was hundred percent. To regulate the discontinuing cause for wearable device cardioverter defibrillator (WCD) use specified that among seven attacked patients received ICD, one patient was died two weeks later discontinuing the use of wearable cardioverter defibrillator device (WCD), and two patients were absent to track. Sixteen were not attacked patients, who obtained an implantable cardioverter defibrillator (ICD) while seven of them attained and improved left ventricular ejection fraction (LVEF). Abrupt heart attack (HA) management amongst patients of cardiac sarcoidosis disease (CS) was assisted by wearable device cardioverter defibrillator (WCD) ensuing in positive ventricular tachycardia or ventricular fibrillation (VT/VF) termination upon attack delivery. In this paper, the dataset is retrieved from google dataset search and evaluated on various ML models to predict the survival of the patients Receiving ICD while wearing WCD as well as evaluating the developed model performance and to identify the best applicable model. Dataset is primarily processed and nursed to many machine learning classifiers like KNN, SVM, Perceptron, Random Forest, Decision Trees (DT), Logistic Expression, SGD, and Naïve Basis. Cross-validation is smeared, training is performed so that new machine learning models are established and verified. The outcomes found are assessed on many factors such as Accuracy, Misclassification Rate, True Positive Rate, True Negative Rate, Precision, Prevalence, False Positive rate taken to build the model. Result analysis reveals that among all the classifiers SVM and KNN best model acquiescent high and precise outcomes.

scholarly journals DMWAS: Deep Machine learning omics Wide Association Study & Feature set optimization by clustering & univariate association for Biomarkers discovery as tested on GTEx pilot dataset for death due to heart-attack

2021 ◽  
Author(s):  
Abhishek Narain Singh
Keyword(s):  
Machine Learning ◽  
Heart Attack ◽  
Association Studies ◽  
Copy Number Variations ◽  
Machine Learning Techniques ◽  
Genome Wide Association Studies ◽  
Structural Variations ◽  
Dna Variation ◽  
Genomic Variants ◽  
Genome Wide

AbstractUnivariate and multivariate methods for association of the genomic variations with the end-or-endo phenotype have been widely used for genome wide association studies. In addition to encoding the SNPs, we advocate usage of clustering as a novel method to encode the structural variations, SVs, in genomes, such as the deletions and insertions polymorphism (DIPs), Copy Number Variations (CNVs), translocation, inversion, etc., that can be used as an independent feature variable value for downstream computation by artificial intelligence methods to predict the endo-or-end phenotype. We introduce a clustering based encoding scheme for structural variations and omics based analysis. We conducted a complete all genomic variants association with the phenotype using deep learning and other machine learning techniques, though other methods such as genetic algorithm can also be applied. Applying this encoding of SVs and one-hot encoding of SNPs on GTEx V7 pilot DNA variation dataset, we were able to get high accuracy using various methods of DMWAS, and particularly found logistic regression to work the best for death due to heart-attack (MHHRTATT) phenotype. The genomic variants acting as feature sets were then arranged in descending order of power of impact on the disease or trait phenotype, which we call optimization and that also uses top univariate association into account. Variant Id P1_M_061510_3_402_P at chromosome 3 & position 192063195 was found to be most highly associated to MHHRTATT. We present here the top ten optimized genomic variant feature set for the MHHRTATT phenotypic cause of death.

scholarly journals A Survey Techniques Used for Prediction of Heart Attack with Machine Learning and Medical Text Mining

2019 ◽  
pp. 108-112
Author(s):  
Divya Yadav ◽  
Gayatri Jain
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Heart Attack ◽  
Human Life ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Healthy People ◽  
Survey Techniques ◽  
Time Diagnosis ◽  
Heart Failure Prevention

Heart attack is one of the most critical heart disease in the world and affects human life very badly. In heart attack, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart attack is important for heart failure prevention and treatment. The diagnosis of such condition through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart attack causes and related problems, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart attack prediction by using heart disease dataset. We used popular machine learning algorithms for performance evaluation metrics such as classification accuracy, sensitivity and correlation coefficient. The proposed system can easily predict and classify people with heart attack possibilities from healthy people.

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