scholarly journals Mapping risk of ischemic heart disease using machine learning in a Brazilian state

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243558
Author(s):  
Marcela Bergamini ◽  
Pedro Henrique Iora ◽  
Thiago Augusto Hernandes Rocha ◽  
Yolande Pokam Tchuisseu ◽  
Amanda de Carvalho Dutra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
The State ◽  
Machine Learning Algorithms ◽  
Ischemic Heart ◽  
Geographical Information ◽  
Support Vector ◽  
P Value ◽  
Municipal Level

Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD based on location and risk factors. Secondary data, geographical information system (GIS) and machine learning were used to validate the HHCI and stratify the IHD municipality risk in the state of Paraná. A positive spatial autocorrelation was found (Moran’s I = 0.6472, p-value = 0.001), showing clusters of high IHD mortality. The Support Vector Machine, which had an RMSE of 0.789 and error proportion close to one (0.867), was the best for prediction among eight machine learning algorithms after validation. In the north and northwest regions of the state, HHCI was low and mortality clusters patterns were high. By creating an HHCI through ML, we can predict IHD mortality rate at municipal level, identifying predictive characteristics that impact health conditions of these localities’ guided health management decisions for improvements for IHD within the emergency care network in the state of Paraná.

A Comparative Study of Machine Learning Algorithms for Diagnosis of Ischemic Heart Disease

2018 ◽  
Vol 45 (4) ◽  
pp. 376-389
Author(s):  
Pyoung-Woo Park ◽  
Min-Koo Kim ◽  
Hong-Seok Lim ◽  
Duk-Yong Yoon ◽  
Seok-Won Lee
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Ischemic Heart

scholarly journals Comprehensive Study of Acute Myocardial Infarctions Triggers

2020 ◽  
Author(s):  
Kamal Khademvatani ◽  
Amin Sedokani ◽  
Sima Masudi ◽  
Parisa Nejati ◽  
Mir Hossein Seyed-Mohammadzad ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Health Care Systems ◽  
Ischemic Heart ◽  
Clinical Event ◽  
P Value ◽  
Cross Sectional

AbstractAimMyocardial infarction (MI) is one of the most important cardiovascular diseases. A trigger is an external stimulus, potential to create a pathological change leading to a clinical event. In addition to classic risk factors of ischemic heart disease and myocardial infarction, MI triggers play critical roles in the incidence of acute MI.Methods and ResultsThis is a cross-sectional study of 254 patients with the first acute myocardial infarction referring to Seyedoshohada heart center of Urmia, Iran were enrolled in the study within one year of study. After 48h of hospitalization and, treatment, and cardiac caring, the patients were provided with the questionnaire to collecting the history of the disease ad triggers. In addition to laboratory and paraclinical data, the analysis of the study was performed. Out of 220 (86.4%) patients with STEMI and 34 (13.6%) patients with NSTEMI, there were significant differences (P-value <0.05) in AMI triggers with LVEF (0.03), gender (0.027), residency and living area (0.039), occupation (0.002), smoking (0.008), abnormal serum TG levels (0.018) and the season of AMI occurrence (0.013). The mean age for AMI patients was 60.4±12.97 years old with a mean BMI of 26.65±4.35 kg/m2.ConclusionIn addition to classic risk factors of ischemic heart disease and myocardial infarction, health care systems and physicians must pay more attention to triggers that may induce an acute myocardial infarction in people with predisposing factors especially in the male sex, stressful and hand working jobs, and psychological and mental tension patients.

scholarly journals Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nalindren Naicker ◽  
Timothy Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Student Performance ◽  
State Of The Art ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Support Vector ◽  
Vector Machines

Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

scholarly journals Effects of copper and zinc on ischemic heart disease and myocardial infarction: a Mendelian randomization study

2018 ◽  
Vol 108 (2) ◽  
pp. 237-242 ◽  
Author(s):  
Hanish P Kodali ◽  
Brian T Pavilonis ◽  
C Mary Schooling
Keyword(s):  
Sensitivity Analysis ◽  
Heart Disease ◽  
Ischemic Heart Disease ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Dietary Factors ◽  
Ischemic Heart ◽  
P Value ◽  
Copper And Zinc ◽  
A Genome

ABSTRACTBackgroundDespite great progress in prevention and control, ischemic heart disease (IHD) remains a leading cause of global morbidity and mortality. Diet plays a key role in IHD, but a comprehensive delineation of the role of dietary factors in IHD is not yet quite complete.ObjectiveThe aim of this study was to test the long-standing hypothesis that copper is protective and zinc harmful in IHD.DesignWe used separate-sample instrumental variable analysis with genetic instruments (Mendelian randomization). We obtained single nucleotide polymorphisms (SNPs) from a genome wide association study, strongly (P value < 5 × 10−8) and independently associated with erythrocyte copper and zinc. We applied these genetic predictors of copper and zinc to the largest, most extensively genotyped IHD case (n ≤ 76014)-control (n ≤ 264785) study, based largely on CARDIoGRAMplusC4D 1000 Genomes and the UK Biobank SOFT CAD, to obtain SNP-specific Wald estimates for the effects of copper and zinc on IHD, which were combined through the use of inverse variance weighting. Sensitivity analysis included use of the MR-Egger method, and reanalysis including SNPs independently associated with erythrocyte copper and zinc at P value < 5 × 10−6.ResultsGenetically instrumented copper was negatively associated with IHD (OR: 0.94; 95% CI: 0.90, 0.98). Genetically instrumented zinc was positively associated with IHD (OR: 1.06; 95% CI: 1.02, 1.11). Sensitivity analysis via MR-Egger gave no indication of unknown pleiotropy; less strongly associated SNPs gave similar results for copper.ConclusionGenetic validation of a long-standing hypothesis suggests that further investigation of the effects, particularly of copper, on IHD may provide a practical means of reducing the leading cause of mortality and morbidity.

scholarly journals (Frequency of CYP2C19 Gene Polymorphisms (SNP) and its Impact on Clinical Outcome in Ischemic Heart Disease Patients Taking Clopidogrel after percutaneous coronary intervention (PCI

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Ahmed Shawky, Mokhtar Elzawahry, Hussein Sabet, Khaled Barak
Keyword(s):  
Heart Disease ◽  
Clinical Outcome ◽  
Ischemic Heart Disease ◽  
Coronary Intervention ◽  
Ischemic Heart ◽  
P Value ◽  
Loss Of Function ◽  
Multicenter Studies ◽  
Non Invasive ◽  
Cyp2c19 Gene

Clopidogrel an oral thienopyridine derivative capable of inhibiting platelet activation. Clopidogrel is prodrug that is converted into an active drug by hepatic cytochrome CYP2C19, CYP2C19*2 and CYP2C19*3 polymorphic alleles are considered to be important loss- of- function alleles resulting in diminished response to clopidogrel. our study aimed to detect frequency of CYP2C19 gene polymorohisms and its impact on clinical outcome in ischemic heart disease patients taking clopidogrel. Matrial and methods: blood samples were collected from 102 ischemic heart disease patients and the frequency alleles was determined by PCR and all patients were followed by clinical assessment and invasive and non- invasive cardiac investigations .Results:. frequency of CYP2C19*1 was 49%, CYP2C19*2 was 15% and CYP2C19*3 was 1%, CYP2C19*17 was 34%, patients with recurrent ischemic attacks 37patients (35.8%), from those patients, 10 patients were normal metabolizer (27%), and 27 patients were abnormal metabolizers(73%) with p- value 0.047 to myocardial infarction and 0.020 to unstable angina. Conclusion:. Asignificance relation was found between CYP2C19polymorphism and recurrent ischemic attacks in this study and multicenter studies are required to confirm this results.

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 Crime Data Forecasting Using Machine Learning and Big Data Analytics

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 591-606
Author(s):  
R. Brindha ◽  
Dr.M. Thillaikarasi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Crime Data

Big data analytics (BDA) is a system based method with an aim to recognize and examine different designs, patterns and trends under the big dataset. In this paper, BDA is used to visualize and trends the prediction where exploratory data analysis examines the crime data. “A successive facts and patterns have been taken in following cities of California, Washington and Florida by using statistical analysis and visualization”. The predictive result gives the performance using Keras Prophet Model, LSTM and neural network models followed by prophet model which are the existing methods used to find the crime data under BDA technique. But the crime actions increases day by day which is greater task for the people to overcome the challenging crime activities. Some ignored the essential rate of influential aspects. To overcome these challenging problems of big data, many studies have been developed with limited one or two features. “This paper introduces a big data introduces to analyze the influential aspects about the crime incidents, and examine it on New York City. The proposed structure relates the dynamic machine learning algorithms and geographical information system (GIS) to consider the contiguous reasons of crime data. Recursive feature elimination (RFE) is used to select the optimum characteristic data. Exploitation of gradient boost decision tree (GBDT), logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) are related to develop the optimum data model. Significant impact features were then reviewed by applying GBDT and GIS”. The experimental results illustrates that GBDT along with GIS model combination can identify the crime ranking with high performance and accuracy compared to existing method.”

scholarly journals PigLeg: prediction of swine phenotype using machine learning

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8764 ◽  
Author(s):  
Siroj Bakoev ◽  
Lyubov Getmantseva ◽  
Maria Kolosova ◽  
Olga Kostyunina ◽  
Duane R. Chartier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Average Daily Gain ◽  
Nearest Neighbors ◽  
The State ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Leg Weakness

Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.

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