BioLearner: A Machine Learning-Powered Smart Heart Disease Risk Prediction System Utilizing Biomedical Markers

2021 ◽  
Author(s):  
Syed Saad Amer ◽  
Gurleen Wander ◽  
Manmeet Singh ◽  
Rami Bahsoon ◽  
Nicholas R. Jennings ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Risk ◽  
Support Vector ◽  
Prediction System ◽  
Developing Heart ◽  
Nearest Neighbours ◽  
Different Types ◽  
History Of ◽  
The Uk

Heart disease kills more people around the world than any other disease, and it is one of the leading causes of death in the UK, triggering up to 74,000 deaths per year. An essential part in the prevention of deaths by heart disease and thus heart disease itself is the analysis of biomedical markers to determine the risk of a person developing heart disease. Lots of research has been conducted to assess the accuracy of detecting heart disease by analyzing biomedical markers. However, no previous study has attempted to identify the biomedical markers which are most important in this identification. To solve this problem, we proposed a machine learning-based intelligent heart disease prediction system called BioLearner for the determination of vital biomedical markers. This study aims to improve upon the accuracy of predicting heart disease and identify the most essential biological markers. This is done with the intention of composing a set of markers that impacts the development of heart disease the most. Multiple factors determine whether or not a person develops heart disease. These factors are thought to include Age, history of chest pain (of different types), fasting blood sugar of different types, heart rate, smoking, and other essential factors. The dataset is analyzed, and the different aspects are compared. Various machine learning models such as [Formula: see text] Nearest Neighbours, Neural Networks, Support Vector Machine (SVM) are trained and used to determine the accuracy of our prediction for future heart disease development. BioLearner is able to predict the risk of heart disease with an accuracy of 95%, much higher than the baseline methods.

scholarly journals Heart Disease Prediction Using Machine Learning

2020 ◽  
Vol 6 (12) ◽  
pp. 290-293
Author(s):  
Tarun Rahuja Nidhi Sengar and Dr.Amita Goe
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forests ◽  
Vital Role ◽  
Support Vector ◽  
Medical Practitioners ◽  
Nearest Neighbours ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Locomotor Disorders

This paper revolves around a classification use case of machine learning in which the intention is to predict the possibility of a heart disease in an individual given certain parameters. Machine Learning is extensively being used across the world. The healthcare industry has also commenced leveraging these data driven techniques. Machine Learning can play a vital role in predicting the likelihood of locomotor disorders, Heart ailments and more such diseases because machine learning is well known for its use cases in classifying, categorizing and predicting. Such information, if predicted well, can provide key foresight to doctors who can hence mould their diagnosis and course of treatment per patient basis. The main advantage of using machine learning in healthcare is its ability to parse and process huge datasets which are beyond the scope of human abilities, and then accurately convert the derived analysis of that data into clinical insights that can aid medical practitioners round the globe in planning stratergies for providing care to patients, ultimately leading to more promising results, reduced costs of care and last but not the least , increased patient satiation and response/recovery. To simplify and solve this problem, solutions were provided using multiple supervised learning algorithms like logistic regression, Naïve Bayes, random forests, decision trees, support vector machines and K-nearest neighbours. The best accuracy was seen using random forests.

A Comparative Study of Two Machine Learning Algorithms for Heart Disease Prediction System

2021 ◽  
Author(s):  
Wan Adlina Husna Wan Azizan ◽  
A'zraa Afhzan Ab Rahim ◽  
Siti Lailatul Mohd Hassan ◽  
Ili Shairah Abdul Halim ◽  
Noor Ezan Abdullah
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
Prediction System

scholarly journals Heart Disease Prediction using Machine Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 700-704
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Nearest Neighbour ◽  
Decision Tree Classifier ◽  
Support Vector Classifier ◽  
Learning Techniques ◽  
Tree Classifier

Deriving the methodologies to detect heart issues at an earlier stage and intimating the patient to improve their health. To resolve this problem, we will use Machine Learning techniques to predict the incidence at an earlier stage. We have a tendency to use sure parameters like age, sex, height, weight, case history, smoking and alcohol consumption and test like pressure ,cholesterol, diabetes, ECG, ECHO for prediction. In machine learning there are many algorithms which will be used to solve this issue. The algorithms include K-Nearest Neighbour, Support vector classifier, decision tree classifier, logistic regression and Random Forest classifier. Using these parameters and algorithms we need to predict whether or not the patient has heart disease or not and recommend the patient to improve his/her health.

scholarly journals Development and Validation of a Novel Dementia Risk Score in the UK Biobank Cohort

2021 ◽  
Author(s):  
Melis Anatürk ◽  
Raihaan Patel ◽  
Georgios Georgiopoulos ◽  
Danielle Newby ◽  
Anya Topiwala ◽  
...  
Keyword(s):  
At Risk ◽  
Cardiovascular Risk ◽  
Risk Score ◽  
Disease Risk ◽  
Risk Index ◽  
Risk Scores ◽  
Uk Biobank ◽  
Dementia Risk ◽  
History Of ◽  
The Uk

INTRODUCTION: Current prognostic models of dementia have had limited success in consistently identifying at-risk individuals. We aimed to develop and validate a novel dementia risk score (DRS) using the UK Biobank cohort.METHODS: After randomly dividing the sample into a training (n=166,487, 80%) and test set (n=41,621, 20%), logistic LASSO regression and standard logistic regression were used to develop the UKB-DRS.RESULTS: The score consisted of age, sex, education, apolipoprotein E4 genotype, a history of diabetes, stroke, and depression, and a family history of dementia. The UKB-DRS had good-to-strong discrimination accuracy in the UKB hold-out sample (AUC [95%CI]=0.79 [0.77, 0.82]) and in an external dataset (Whitehall II cohort, AUC [95%CI]=0.83 [0.79,0.87]). The UKB-DRS also significantly outperformed four published risk scores (i.e., Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI), Cardiovascular Risk Factors, Aging, and Dementia score (CAIDE), Dementia Risk Score (DRS), and the Framingham Cardiovascular Risk Score (FRS) across both test sets.CONCLUSION: The UKB-DRS represents a novel easy-to-use tool that could be used for routine care or targeted selection of at-risk individuals into clinical trials.

scholarly journals Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

2020 ◽  
Vol 9 (3) ◽  
pp. 2758-2762
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Clinical Data ◽  
Disease Risk ◽  
Learning Algorithm ◽  
Information Storage ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

scholarly journals Faktor Risiko Terjadinya Penyakit Jantung Koroner pada Pasien di Unit Rawat Jalan Rumah Sakit Panti Rapih Yogyakarta

2020 ◽  
Vol 1 (1) ◽  
pp. 21-30
Author(s):  
Deviana Widayanti ◽  
Chatarina Setya Widyastuti
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Family History ◽  
Disease Risk ◽  
Sampling Technique ◽  
Excess Body Weight ◽  
History Of ◽  
Lack Of Exercise

Background: Coronary Heart Disease (CHD) Is a condition when the arteries that supply blood to the heart wall experience hardening and narrowing. It is estimated that 30% of coronary heart disease causes death worldwide. Objective: This study aims to determine the risk factors for CHD in Panti Rapih Hospital. Methods: This descriptive study aims to determine the risk factors for CHD in outpatients at Panti Rapih Hospital. The population is patients who have been diagnosed with coronary heart disease and the sample was taken by 50 respondents with non-random accidental sampling technique. This research take the data use questionnaire and make univariat analysis. Results: Risk factors for CHD are a number of factors that cannot be changed: family history of 42%, age = 40 years 95% in men and 95% age = 65 years in women. Factors that can be changed are: Smoking 78%, history of hypertension 68%, history of diabetes mellitus 28%, dyslipidemic 90%, excess body weight42% and lack of exercise 38%. Conclusion: Risk factors for CHD that cannot be changed: family history of 42%, age = 40 years 95% in men and 95% age = 65 years in women. Factors that can be changed are: Smoking 78%, history of hypertension 68%, history of diabetes mellitus 28%, dyslipidemic 90%, excess body weight 42% and lack of exercise 38%.     Keywords: coronary heart disease, risk factors

scholarly journals Determination of Significant Features for Building an Efficient Heart Disease Prediction System

2019 ◽  
Vol 8 (2) ◽  
pp. 4499-4504
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Prediction System ◽  
Early Stages

Heart diseases are responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.

scholarly journals An Improved Machine Learning Technique with Effective Heart Disease Prediction System

2021 ◽  
Vol 69 (3) ◽  
pp. 4169-4181
Author(s):  
Mohammad Tabrez Quasim ◽  
Saad Alhuwaimel ◽  
Asadullah Shaikh ◽  
Yousef Asiri ◽  
Khairan Rajab ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Prediction ◽  
Prediction System ◽  
Machine Learning Technique ◽  
Learning Technique

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.

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