A Machine Learning Approach: Using Predictive Analytics to Identify and Analyze High Risks Patients with Heart Disease

Khennou, Fadoua; Fahim, Charif; Chaoui, Habiba; Chaoui, Nour El Houda

doi:10.18178/ijmlc.2019.9.6.870

Cited by 19 publications

(8 citation statements)

References 10 publications

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“…Similarly, the test accuracy of improved logistic regression model gave 100% against that of the standard logistic regression which was 94.74%. In line with the conventional practice in most machine learning projects, 70% of the dataset was used for training, while 30% was used for testing [20]. The improved logistic regression classifier showed good performance judging by Table III as well as Fig.…”

Section: A Performance Evaluationmentioning

confidence: 76%

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Towards Machine Learning Based Analysis of Quality of User Experience (QoUE)

Nwakanma¹,

Hossain²,

Lee³

et al. 2020

IJMLC

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Industries use various platforms to receive feedback from users of their products. In this paper, there is an overview of the potentials of using natural language processing system (NLP) in classifying the quality of user experience. The user experience is captured using google form. To test the efficacy of the platform, sentiments of users were analysed using hotels.ng as the source of data. The natural processing of electronic word of mouth (e-WOM) can be applied to any feedback platforms to classify and predict customers' sentiments and provide a veritable opportunity for companies to capture the quality of users' experiences and improve service delivery. The feature or sentiments extraction was done using opinion mining and data cleaning tools on heterogeneous data sources to judge the decision-making process of users. Using charts and correlations, with an average performance level of the willingness to recommend and degree of review helpfulness, the platform showed that the Quality of User Experience (QoUE) of the customers are 7.31 and 7.03 respectively. Finally, an improved logistic regression classifier was developed to test, train and classify the user experiences. Comparing the improved logistic regression classifier with standard logistic regression classifier shows that the training accuracy of the proposed improved logistic regression gave 97.67% as against the standard logistic regression which had accuracy of 86.01%

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Section: A Performance Evaluationmentioning

confidence: 76%

“…This paper compared improved logistic regression classifier with standard logistic regression. The evaluation of the model accuracy was done using the standard Equation (2) adapted from [20]. In Equation ( 2), ε is the number of true positives while α is the number of false positives.…”

Section: A Overall Analytics Proceduresmentioning

confidence: 99%

Towards Machine Learning Based Analysis of Quality of User Experience (QoUE)

Nwakanma¹,

Hossain²,

Lee³

et al. 2020

IJMLC

View full text Add to dashboard Cite

show abstract

“…These machine learning techniques significantly differ from conventional IT approaches as they rely on the learning process itself and extract specific behaviors from data to address various issues. Their versatility and ability to improve performance based on encountered data make them valuable tools across multiple fields [25].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students

Musleh,

Alkhwaja,

Alkhwaja

et al. 2024

BDCC

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Obesity is increasingly becoming a prevalent health concern among adolescents, leading to significant risks like cardiometabolic diseases (CMDs). The early discovery and diagnosis of CMD is essential for better outcomes. This study aims to build a reliable artificial intelligence model that can predict CMD using various machine learning techniques. Support vector machines (SVMs), K-Nearest neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting are five robust classifiers that are compared in this study. A novel “risk level” feature, derived through fuzzy logic applied to the Conicity Index, as a novel feature, which was previously unused, is introduced to enhance the interpretability and discriminatory properties of the proposed models. As the Conicity Index scores indicate CMD risk, two separate models are developed to address each gender individually. The performance of the proposed models is assessed using two datasets obtained from 295 records of undergraduate students in Saudi Arabia. The dataset comprises 121 male and 174 female students with diverse risk levels. Notably, Logistic Regression emerges as the top performer among males, achieving an accuracy score of 91%, while Gradient Boosting lags with a score of 72%. Among females, both Support Vector Machine and Logistic Regression lead with an accuracy score of 87%, while Random Forest performs least optimally with a score of 80%.

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“…When testing a heart failure dataset, it was found that LEBoosting method was highly effective at classifying compared to other methods such as hybrid machine learning, ensemble learning and deep learning. When testing a heart failure dataset, it was found that LEBoosting method was highly effective at classifying compared to other methods such as hybrid machine learning, ensemble learning and deep learning by comparison, it was found that LEBoosting had a higher efficiency than hybrid machine learning, i.e., research [24][25][26][27] because LEBoosting is a combination model from various machine learning. This is like combining the outstanding capabilities of each algorithm together.…”

Section: Figure 3 Leboosting Classification Confusion Matrixmentioning

confidence: 99%

A New Efficiency Improvement of Ensemble Learning for Heart Failure Classification by Least Error Boosting

2022

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Heart failure is a very common disease, often a silent threat. It's also costly to treat and detect. There is also a steadily higher incidence rate of the disease at present. Although researchers have developed classification algorithms. Cardiovascular disease data were used by various ensemble learning methods, but the classification efficiency was not high enough due to the cumulative error that can occur from any weak learner effect and the accuracy of the vote-predicted class label. The objective of this research is the development of a new algorithm that improves the efficiency of the classification of patients with heart failure. This paper proposes Least Error Boosting (LEBoosting), a new algorithm that improves adaboost.m1's performance for higher classification accuracy. The learning algorithm finds the lowest error among various weak learners to be used to identify the lowest possible errors to update distribution to create the best final hypothesis in classification. Our trial will use the heart failure clinical records dataset, which contains 13 features of cardiac patients. Performance metrics are measured through precision, recall, f-measure, accuracy, and the ROC curve. Results from the experiment found that the proposed method had high performance compared to naïve bayes, k-NN,and decision tree, and outperformed other ensembles including bagging, logitBoost, LPBoost, and adaboost.m1, with an accuracy of 98.89%, and classified the capabilities of patients who died accurately as well compared to decision tree and bagging, which were completely indistinguishable. The findings of this study found that LEBoosting was able to maximize error reductions in the weak learner's training process from any weak learner to maximize the effectiveness of cardiology classifiers and to provide theoretical guidance to develop a model for analysis and prediction of heart disease. The novelty of this research is to improve original ensemble learning by finding the weak learner with the lowest error in order to update the best distribution to the final hypothesis, which will give LEBoosting the highest classification efficiency. Doi: 10.28991/ESJ-2023-07-01-010 Full Text: PDF

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A Machine Learning Approach: Using Predictive Analytics to Identify and Analyze High Risks Patients with Heart Disease

Cited by 19 publications

References 10 publications

Towards Machine Learning Based Analysis of Quality of User Experience (QoUE)

Towards Machine Learning Based Analysis of Quality of User Experience (QoUE)

Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students

A New Efficiency Improvement of Ensemble Learning for Heart Failure Classification by Least Error Boosting

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