Prediction of Chronic and Infectious Diseases using Machine Learning Classifiers- A Systematic Approach

Kumar, N. Komal; Sikamani, K. Thirunadana

doi:10.22266/ijies2020.0831.02

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…ML classification techniques to forecast chronic illness were used in [ 23 ]. The Hoeffding classifier correctly predicted heart disease with an accuracy of 88.56% in their study.…”

Section: Methodsmentioning

confidence: 99%

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Hassan

Iqbal

Irfan

et al. 2022

Sensors

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Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.

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“…ML classification techniques to forecast chronic illness were used in [ 23 ]. The Hoeffding classifier correctly predicted heart disease with an accuracy of 88.56% in their study.…”

Section: Methodsmentioning

confidence: 99%

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Hassan

Iqbal

Irfan

et al. 2022

Sensors

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“…Weedy classifiers worked better when they used bagging and boosting, and their ability to predict cardiovascular disease risk was rated well when they worked together. They made the hybrid model by using majority voting with the Bayes Net, NB, C4.5, MLP, and RF classifiers 19 . With 85.48 percent of the time, the model that was made is right.…”

Section: Literature Overviewmentioning

confidence: 99%

An active learning machine technique based prediction of cardiovascular heart disease from UCI-repository database

Srinivasan,

Gunasekaran,

Mathivanan

et al. 2023

Sci Rep

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Heart disease is a significant global cause of mortality, and predicting it through clinical data analysis poses challenges. Machine learning (ML) has emerged as a valuable tool for diagnosing and predicting heart disease by analyzing healthcare data. Previous studies have extensively employed ML techniques in medical research for heart disease prediction. In this study, eight ML classifiers were utilized to identify crucial features that enhance the accuracy of heart disease prediction. Various combinations of features and well-known classification algorithms were employed to develop the prediction model. Neural network models, such as Naïve Bayes and Radial Basis Functions, were implemented, achieving accuracies of 94.78% and 90.78% respectively in heart disease prediction. Among the state-of-the-art methods for cardiovascular problem prediction, Learning Vector Quantization exhibited the highest accuracy rate of 98.7%. The motivation behind predicting Cardiovascular Heart Disease lies in its potential to save lives, improves health outcomes, and allocates healthcare resources efficiently. The key contributions encompass early intervention, personalized medicine, technological advancements, the impact on public health, and ongoing research, all of which collectively work toward reducing the burden of CHD on both individual patients and society as a whole.

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“…Notably, this approach demonstrated a notable improvement of 2.1 percent in accuracy. Similarly, the author discussed another relevant study, Kumar & Sikamani (2020) , focused on predicting chronic heart disease. The UCI repository served as the dataset for this study, wherein a machine-learning approach was employed.…”

Section: Related Workmentioning

confidence: 99%

Heart failure survival prediction using novel transfer learning based probabilistic features

Qadri,

Hashmi,

Raza

et al. 2024

PeerJ Computer Science

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Heart failure is a complex cardiovascular condition characterized by the heart’s inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques. We analyzed data from 299 hospitalized heart failure patients, addressing the issue of imbalanced data with the Synthetic Minority Oversampling (SMOTE) method. Additionally, we proposed a novel transfer learning-based feature engineering approach that generates a new probabilistic feature set from patient data using ensemble trees. Nine fine-tuned machine learning models are built and compared to evaluate performance in patient survival prediction. Our novel transfer learning mechanism applied to the random forest model outperformed other models and state-of-the-art studies, achieving a remarkable accuracy of 0.975. All models underwent evaluation using 10-fold cross-validation and tuning through hyperparameter optimization. The findings of this study have the potential to advance the field of cardiovascular medicine by providing more accurate and personalized prognostic assessments for individuals with heart failure.

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Prediction of Chronic and Infectious Diseases using Machine Learning Classifiers- A Systematic Approach

Cited by 13 publications

References 18 publications

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

An active learning machine technique based prediction of cardiovascular heart disease from UCI-repository database

Heart failure survival prediction using novel transfer learning based probabilistic features

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