Heart Disease Prediction Using Machine Learning  Techniques

Yadav, Anurag

doi:10.17762/itii.v9i1.120

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…They have reported the highest accuracy as 74.4 %for the classification algorithm Random Forest and the lowest accuracy in this work is attained by the KNN reported as 71.3%. [4]The work "Understanding the lifestyle of people to identify the reasons for Diabetes using data mining" proposed by Gavin Pinto, Radhika Desai, and Sunil Jangid discussed reducing the risk of diabetes disease using data mining techniques and also discussed diabetes sub-classification. The authors used Naïve Bayes and SVM classification algorithms on the dataset collected by a survey using google forms and reported the accuracy of 64.92 for SVM and 60.44 for Naïve Bayes.…”

Section: IIImentioning

confidence: 99%

Machine Learning Based Multiple Disease Prediction using Streamlit

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Srivastav,

Gaud

et al. 2024

IJRASET

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There are multiple techniques in machine learning that can in a variety of industries, do predictive analytics on large amounts of data. Predictive analytics in healthcare is a difficult endeavour, but it can eventually assist practitioners in making timely decisions regarding patients' health and treatment based on massive data. Diseases like Breast cancer, diabetes, and heart- related diseases are causing many deaths globally but most of these deaths are due to the lack of timely check-ups of the diseases. The above problem occurs due to a lack of medical infrastructure and a low ratio of doctors to the population. The statistics clearly show the same, WHO recommended, the ratio of doctors to patients is 1:1000 whereas India’s doctor-to- population ratio is 1:1456, this indicates the shortage of doctors. The diseases related to heart, cancer, and diabetes can cause a potential threat to mankind, if not found early. Therefore, early recognition and diagnosis of these diseases can save a lot of lives. This work is all about predicting diseases that are harmful using machine learning classification algorithms. In this work, parkinsons, heart, and diabetes are included. To make this work seamless and usable by the mass public, our team made a medical test web application that makes predictions about various diseases using the concept of machine learning. In this work, our aim to develop a disease-predicting web app that uses the concept of machine learning-based predictions about various diseases like Parkinson, Diabetes, and Heart disease

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Section: IIImentioning

confidence: 99%

Machine Learning Based Multiple Disease Prediction using Streamlit

More,

Srivastav,

Gaud

et al. 2024

IJRASET

View full text Add to dashboard Cite

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“…The comparison was made between the ML algorithm and RF achieves the highest accuracy with 82%. Yadav et al (16) proposed an optimization model, namely, "Optimized DNN using Talos" deploy ML classifiers, such as DT, KNN, RF, and Ensemble model (used ANN, KNN, and Support Vector Machine, SVM), for the prediction of heart diseases. They have used the concept of dimensional reduction where only vital information of patients was considered.…”

Section: Related Study Literature Surveymentioning

confidence: 99%

Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

Shah

Ahirrao

Pandya

et al. 2021

Front. Public Health

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Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform, such as Google Firebase. The acquired data is then classified using six machine-learning algorithms: Artificial Neural Network (ANN), Random Forest Classifier (RFC), Gradient Boost Extreme Gradient Boosting (XGBoost) classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox regression survival analysis methodologies for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level of performance with an overall accuracy of 98% using DT on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for gender- and age-wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.

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A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

et al. 2022

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In medical filed, predicting the occurrence of heart diseases is a significant piece of work. Millions of healthcare-related complexities that have remained unsolved up until now can be greatly simplified with the help of machine learning. The proposed study is concerned with the cardiac disease diagnosis decision support system. An OpenML repository data stream with 1 million instances of heart disease and 14 features is used for this study. After applying to preprocess and feature engineering techniques, machine learning approaches like random forest, decision trees, gradient boosted trees, linear support vector classifier, logistic regression, one-vs-rest, and multilayer perceptron are used to perform binary and multiclassification on the data stream. When combined with the Max Abs Scaler technique, the multilayer perceptron performed satisfactorily in both binary (Accuracy 94.8%) and multiclassification (accuracy 88.2%). Compared to the other binary classification algorithms, the GBT delivered the right outcome (accuracy of 95.8%). Multilayer perceptrons, however, did well in multiple classifications. Techniques such as oversampling and undersampling have a negative impact on disease prediction. Machine learning methods like multilayer perceptrons and ensembles can be helpful for diagnosing cardiac conditions. For this kind of unbalanced data stream, sampling techniques like oversampling and undersampling are not practical.

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Heart Disease Prediction Using Machine Learning Techniques

Cited by 6 publications

References 6 publications

Machine Learning Based Multiple Disease Prediction using Streamlit

Machine Learning Based Multiple Disease Prediction using Streamlit

Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

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