scholarly journals A Machine Learning Approach for Predicting Heart Disease using Efficient Algorithm

2021 ◽  
Author(s):  
Anuradha Thippanna ◽  
Priyanka Vutkur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Efficient Algorithm ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals An Analytical Model for Prediction of Heart Disease using Machine Learning Classifiers

2021 ◽  
Author(s):  
Diti Roy ◽  
Md. Ashiq Mahmood ◽  
Tamal Joyti Roy
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithm ◽  
Modern Technology ◽  
Learning Approach ◽  
Data Sets ◽  
Machine Learning Classifiers ◽  
Machine Learning Approach ◽  
Day By Day

<p>Heart Disease is the most dominating disease which is taking a large number of deaths every year. A report from WHO in 2016 portrayed that every year at least 17 million people die of heart disease. This number is gradually increasing day by day and WHO estimated that this death toll will reach the summit of 75 million by 2030. Despite having modern technology and health care system predicting heart disease is still beyond limitations. As the Machine Learning algorithm is a vital source predicting data from available data sets we have used a machine learning approach to predict heart disease. We have collected data from the UCI repository. In our study, we have used Random Forest, Zero R, Voted Perceptron, K star classifier. We have got the best result through the Random Forest classifier with an accuracy of 97.69.<i><b></b></i></p> <p><b> </b></p>

scholarly journals Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jingyi Zhang ◽  
Huolan Zhu ◽  
Yongkai Chen ◽  
Chenguang Yang ◽  
Huimin Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Speckle Tracking ◽  
Speckle Tracking Echocardiography ◽  
Screening Tools ◽  
Learning Approach ◽  
Classification Models ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach

Abstract Background Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate. We use 64 two-dimensional speckle tracking echocardiography (2D-STE) features and seven clinical features to predict whether one has CHD. Methods We develop a machine learning approach that integrates a number of popular classification methods together by model stacking, and generalize the traditional stacking method to a two-step stacking method to improve the diagnostic performance. Results By borrowing strengths from multiple classification models through the proposed method, we improve the CHD classification accuracy from around 70–87.7% on the testing set. The sensitivity of the proposed method is 0.903 and the specificity is 0.843, with an AUC of 0.904, which is significantly higher than those of the individual classification models. Conclusion Our work lays a foundation for the deployment of speckle tracking echocardiography-based screening tools for coronary heart disease.

scholarly journals An Analytical Model for Prediction of Heart Disease using Machine Learning Classifiers

2021 ◽  
Author(s):  
Diti Roy ◽  
Md. Ashiq Mahmood ◽  
Tamal Joyti Roy
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithm ◽  
Modern Technology ◽  
Learning Approach ◽  
Data Sets ◽  
Machine Learning Classifiers ◽  
Machine Learning Approach ◽  
Day By Day

<p>Heart Disease is the most dominating disease which is taking a large number of deaths every year. A report from WHO in 2016 portrayed that every year at least 17 million people die of heart disease. This number is gradually increasing day by day and WHO estimated that this death toll will reach the summit of 75 million by 2030. Despite having modern technology and health care system predicting heart disease is still beyond limitations. As the Machine Learning algorithm is a vital source predicting data from available data sets we have used a machine learning approach to predict heart disease. We have collected data from the UCI repository. In our study, we have used Random Forest, Zero R, Voted Perceptron, K star classifier. We have got the best result through the Random Forest classifier with an accuracy of 97.69.<i><b></b></i></p> <p><b> </b></p>

scholarly journals A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

2021 ◽  
Vol 67 (1) ◽  
pp. 51-71 ◽  
Author(s):  
Mohamed Elhoseny ◽  
Mazin Abed Mohammed ◽  
Salama A. Mostafa ◽  
Karrar Hameed Abdulkareem ◽  
Mashael S. Maashi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Multi Agent ◽  
Heart Disease Diagnosis

scholarly journals Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yaozhong Liu ◽  
Fan Bai ◽  
Zhenwei Tang ◽  
Na Liu ◽  
Qiming Liu
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Heart Disease ◽  
Valvular Heart Disease ◽  
Meta Analysis ◽  
Learning Approach ◽  
Training Set ◽  
Test Set ◽  
Validation Test ◽  
Machine Learning Approach

Abstract Background Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learning approach. Methods At the transcriptomic level, four microarray datasets (GSE41177, GSE79768, GSE115574, GSE14975) were downloaded from the Gene Expression Omnibus database, which included 130 available atrial samples from AF and sinus rhythm (SR) patients with valvular heart disease. Microarray meta-analysis was adopted to identified differentially expressed genes (DEGs). At the proteomic level, a qualitative and quantitative analysis of proteomics in the left atrial appendage of 18 patients (9 with AF and 9 with SR) who underwent cardiac valvular surgery was conducted. The machine learning correlation-based feature selection (CFS) method was introduced to selected feature genes of AF using the training set of 130 samples involved in the microarray meta-analysis. The Naive Bayes (NB) based classifier constructed using training set was evaluated on an independent validation test set GSE2240. Results 863 DEGs with FDR < 0.05 and 482 differentially expressed proteins (DEPs) with FDR < 0.1 and fold change > 1.2 were obtained from the transcriptomic and proteomic study, respectively. The DEGs and DEPs were then analyzed together which identified 30 biomarkers with consistent trends. Further, 10 features, including 8 upregulated genes (CD44, CHGB, FHL2, GGT5, IGFBP2, NRAP, SEPTIN6, YWHAQ) and 2 downregulated genes (TNNI1, TRDN) were selected from the 30 biomarkers through machine learning CFS method using training set. The NB based classifier constructed using the training set accurately and reliably classify AF from SR samples in the validation test set with a precision of 87.5% and AUC of 0.995. Conclusion Taken together, our present work might provide novel insights into the molecular mechanism and provide some promising diagnostic and therapeutic targets of AF.

scholarly journals TimeTeller: a New Tool for Precision Circadian Medicine and Cancer Prognosis

2019 ◽  
Author(s):  
Denise Vlachou ◽  
Georg A. Bjarnason ◽  
Sylvie Giacchetti ◽  
Francis Lévi ◽  
David A. Rand
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Heart Disease ◽  
Prognostic Factors ◽  
Circadian Clock ◽  
Biological Sample ◽  
Cancer Prognosis ◽  
Learning Approach ◽  
Therapeutic Outcomes ◽  
Machine Learning Approach

AbstractRecent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart disease. Therefore, there is a need for tools to measure the functional state of the circadian clock and its downstream targets in patients. We provide such a tool and demonstrate its clinical relevance by an application to breast cancer where we find a strong link between survival and our measure of clock dysfunction. We use a machine-learning approach and construct an algorithm called TimeTeller which uses the multi-dimensional state of the genes in a transcriptomics analysis of a single biological sample to assess the level of circadian clock dysfunction. We demonstrate how this can distinguish healthy from malignant tissues and demonstrate that the molecular clock dysfunction metric is a potentially new prognostic and predictive breast cancer biomarker that is independent of the main established prognostic factors.

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