A Classification Approach for Heart Disease Diagnosis using Machine Learning

Jeny, J. Rethna Virgil; Reddy, Nalla Sreeja; Aishwarya, Parkhi; Samreen, .

doi:10.1109/ispcc53510.2021.9609468

Cited by 15 publications

(3 citation statements)

References 13 publications

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“…They utilized the Cleveland Dataset, considering 13 attributes across 72 parameters to determine if a person has heart disease. Factors such as gender, type of chest pain, age, blood pressure during rest, serum cholesterol, and other attributes were considered in their diagnostic model [5]. www.ijacsa.thesai.org F. Rabbi presented the most popular categorization models in data mining, employing 'MATLAB multi-layered' of the level feed-forward back-propagation with K-NN, ANN, and SVM.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Cardiovascular Disease using Machine Learning Algorithms

Joshi,

Dembla,

Bhatia

2024

IJACSA

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Heart is the most critical organ of our body for being responsible for regulating and maintaining the blood circulation levels. Globally, heart disease cases are prevalent and constitute a significant cause of mortality. Manifestations such as chest discomfort and irregular heartbeat are notable symptoms. The healthcare sector has amassed substantial knowledge in this domain. Analyzing the research, this paper delves into the concept of utilizing ML algorithms to predict cardiac diseases. In this research will employ a diverse array of machine learning techniques, including decision tree, support vector classifier, random forest, K-NN, logistic regression and naive Bayes. These algorithms utilize specific characteristics to forecast cardiac diseases effectively. Leveraging machine learning algorithms to analyze and predict outcomes from the extensive healthcaregenerated data shows considerable promise. Recent advancements in machine learning models have incorporated numerous features, and in this study, propose the integration of these features in machine learning algorithms to forecast cardiovascular ailments. The main objective of this research is to identify the performance of the mentioned machine learning algorithms for predicting cardiovascular elements.

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Section: Related Workmentioning

confidence: 99%

Prediction of Cardiovascular Disease using Machine Learning Algorithms

Joshi,

Dembla,

Bhatia

2024

IJACSA

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“…DT can handle category and numerical information [75]. In several research publications, DT is used to develop models that predict output variable values based on multiple input variables, and this algorithm produces decisions depending on the training data it was trained on [76]. Regarding the area of pipeline failure risk prediction, Mazumder et al [77] extended non-temporal applications by employing an array of models, including KNN, DT, RF, Naïve Bayes (NB), AdaBoost, XGBoost, Light Gradient Boosting Machine (LGBM), and CatBoost.…”

Section: Application Of Decision Tree Random Forest and Hybrid Modelsmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

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Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

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“…DT can handle categorical and numerical information [ 79 ]. In several research publications, DT was used to develop models that predict output variable values based on multiple input variables, and this algorithm produced decisions depending on the training data it was trained on [ 80 ]. Regarding the area of pipeline failure risk prediction, Mazumder et al [ 81 ] extended non-temporal applications by employing an array of models, including the KNN, DT, RF, Naïve Bayes (NB), AdaBoost, XGBoost, Light Gradient Boosting Machine (LGBM), and CatBoost.…”

Section: Predicted Analytics Models For Oandgmentioning

confidence: 99%

A Review of Predictive Analytics Models in the Oil and Gas Industries

R Azmi,

Yusoff,

Mohd Sallehud-din

2024

Sensors

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Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.

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A Classification Approach for Heart Disease Diagnosis using Machine Learning

Cited by 15 publications

References 13 publications

Prediction of Cardiovascular Disease using Machine Learning Algorithms

Prediction of Cardiovascular Disease using Machine Learning Algorithms

Predictive Analytics Performance on Oil and Gas: A Significant Review

A Review of Predictive Analytics Models in the Oil and Gas Industries

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