An automatic early risk classification of hard coronary heart diseases using framingham scoring model

Elsayed, Hoda Ahmed Galal; Syed, Liyakathunisa

doi:10.1145/3018896.3036384

Cited by 18 publications

(13 citation statements)

References 18 publications

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“…In the proposed paper, Random Forest achieved an accuracy of 84.81%. Prediction of CVDs using KNN and Random Forest was propounded by Hoda et al [19]. In the proposed approach, KNN and Random Forest were used for classification.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…Random Forest achieved an accuracy of 84.81%. Hoda et al [19] has used the Framingham scoring model for the validation of their framework. The algorithms which were included in the experimentation are KNN and random forest and it was observed that accuracy given by the KNN (66.7%) was relatively higher than that of the Random forest (63.49%).…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases

et al. 2021

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Cardiovascular diseases are considered as the most life-threatening syndromes with the highest mortality rate globally. Over a period of time, they have become very common and are now overstretching the healthcare systems of countries. The major factors of cardiovascular diseases are high blood pressure, family history, stress, age, gender, cholesterol, Body Mass Index (BMI), and unhealthy lifestyle. Based on these factors, researchers have proposed various approaches for early diagnosis. However, the accuracy of proposed techniques and approaches needs certain improvements due to the inherent criticality and life threatening risks of cardiovascular diseases. In this article, a MaLCaDD (Machine Learning based Cardiovascular Disease Diagnosis) framework is proposed for the effective prediction of cardiovascular diseases with high precision. Particularly, the framework first deals with the missing values (via mean replacement technique) and data imbalance (via Synthetic Minority Over-sampling Technique -SMOTE). Subsequently, Feature Importance technique is utilized for feature selection. Finally, an ensemble of Logistic Regression and K-Nearest Neighbor (KNN) classifiers is proposed for prediction with higher accuracy. The validation of framework is performed through three benchmark datasets (i.e. Framingham, Heart Disease and Cleveland) and the accuracies of 99.1%, 98% and 95.5 % are achieved respectively. Finally, the comparative analysis prove that MaLCaDD predictions are more accurate (with reduced set of features) as compared to the existing state-of-the-art approaches. Therefore, MaLCaDD is highly reliable and can be applied in real environment for the early diagnosis of cardiovascular diseases.

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Section: A Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases

et al. 2021

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“…One more technique that can be exploited to obtain better performance with some models like K-Nearest Neighbors, Support Vector Machines (SVMs), Naive Bayes, and Neural Networks (see Section 4 for an introduction to these models) is feature normalization. It consists of rescaling all the numerical features to have them on the same scale, thus allowing the ML algorithms that exploit numerical methods (e.g., gradient descent, distance-based algorithm) to work better way 18,19,27,[38][39][40] . We can apply feature normalization in several ways.…”

Section: Feature Normalizationmentioning

confidence: 99%

Machine learning for cardiology

Arfat

Mittone

Esposito

et al. 2022

Minerva Cardiol Angiol

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This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.

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“…The Framingham Heart Study dataset is the first long-term epidemiological study concerned with the possible causes of cardiovascular disease that began in 1948 in Framingham, Massachusetts [20]. The Framingham Heart Study dataset identified the prospective risk factors of cardiovascular diseases and their effects [20], [25].…”

Section: A Datasetmentioning

confidence: 99%

The Role of Data Pre-processing Techniques in Improving Machine Learning Accuracy for Predicting Coronary Heart Disease

Sami¹,

Elsheikh²,

Almasalha³

2021

IJACSA

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These days, in light of the rapid developments, people work day and night to live at a good level. This often causes them to not pay much attention to a healthy lifestyle, such as what they eat or even what physical activities they do. These people are often the most likely to suffer from coronary heart disease. The heart is a small organ responsible for pumping oxygen-rich blood to the rest of the human body through the coronary arteries. Accordingly, any blockage or narrowing in one of these coronary arteries may cause blood not to be pumped to the heart and from it to the rest of the body, and thus cause what is known as heart attacks. From here, the importance of early prediction of coronary heart disease has emerged, as it can help these people change their lifestyle and eating habits to become healthier and thus prevent coronary heart disease and avoid death. This paper improve the accuracy of machine learning techniques in predicting coronary heart disease using data preprocessing techniques. Data preprocessing is a technique used to improve the efficiency of a machine learning model by improving the quality of the feature. The popular Framingham Heart Study dataset was used for validation purposes. The results of the research paper indicate that the use of data preprocessing techniques had a role in improving the predictive accuracy of poorly efficient classifiers, and shows satisfactory performance in determining the risk of coronary heart disease. For example, the Decision Tree classifier led to a predictive accuracy of coronary heart disease of 91.39% with an increase of 1.39% over the previous work, the Random Forest classifier led to a predictive accuracy of 92.80% with an increase of 2.7% over the previous work, the K-Nearest Neighbor classifier led to a predictive accuracy of 92.68% with an increase of 2.58% over the previous work, the Multilayer Perceptron Neural Network (MLP) classifier led to a predictive accuracy of 92.64% with an increase of 2.64% over the previous work, and the Naïve Bayes classifier led to a predictive accuracy of 90.56% with an increase of 0.66% over the previous work.

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An automatic early risk classification of hard coronary heart diseases using framingham scoring model

Cited by 18 publications

References 18 publications

An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases

An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases

Machine learning for cardiology

The Role of Data Pre-processing Techniques in Improving Machine Learning Accuracy for Predicting Coronary Heart Disease

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