Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction

Padmanabhan, Meghana; Yuan, Pengyu; Chada, Govind; Nguyen, Hien Van

doi:10.3390/jcm8071050

Cited by 55 publications

(33 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meghana et al [21] used "auto-sklearn", an automatic machine learning (AutoML) library for developing classifiers of CVDs. They experimented on both the heart UCI dataset and a cardiovascular disease dataset consisting of 70,000 records of patients and, as a result, AutoML outperformed traditional machine learning classifiers.…”

Section: Related Workmentioning

confidence: 99%

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

Chatterjee

Gerdes

Martínez

2020

Sensors

143

107

View full text Add to dashboard Cite

Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and “obesity/overweight” is one of the consequences. “Obesity and overweight” are one of the major lifestyle diseases that leads to other health conditions, such as cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the age and socio-economic background of human beings. The “World Health Organization” (WHO) has anticipated that 30% of global death will be caused by lifestyle diseases by 2030 and it can be prevented with the appropriate identification of associated risk factors and behavioral intervention plans. Health behavior change should be given priority to avoid life-threatening damages. The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a public repository related to lifestyle diseases, such as obesity, CVDs, and diabetes type II. In this study, we targeted people, both male and female, in the age group of >20 and <60, excluding pregnancy and genetic factors. This paper qualifies as a tutorial article on how to use different ML methods to identify potential risk factors of obesity/overweight. Although institutions such as “Center for Disease Control and Prevention (CDC)” and “National Institute for Clinical Excellence (NICE)” guidelines work to understand the cause and consequences of overweight/obesity, we aimed to utilize the potential of data science to assess the correlated risk factors of obesity/overweight after analyzing the existing datasets available in “Kaggle” and “University of California, Irvine (UCI) database”, and to check how the potential risk factors are changing with the change in body-energy imbalance with data-visualization techniques and regression analysis. Analyzing existing obesity/overweight related data using machine learning algorithms did not produce any brand-new risk factors, but it helped us to understand: (a) how are identified risk factors related to weight change and how do we visualize it? (b) what will be the nature of the data (potential monitorable risk factors) to be collected over time to develop our intended eCoach system for the promotion of a healthy lifestyle targeting “obesity and overweight” as a study case in the future? (c) why have we used the existing “Kaggle” and “UCI” datasets for our preliminary study? (d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics?

show abstract

Section: Related Workmentioning

confidence: 99%

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

Chatterjee

Gerdes

Martínez

2020

Sensors

143

107

View full text Add to dashboard Cite

show abstract

“…This dataset contains in total 303 patient records with 76 attributes for each one, but only 14 of them are used for our evaluation to make our scores comparable to previous works. In particular, the Cleveland dataset is the only one that has been used by ML researchers to this date [6], [7], [12], [13], [22], [23], [30][31][32][33]. Tab.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Efficient heart disease diagnosis based on twin support vector machine

Brik¹,

Djerioui²,

Attallah³

2021

Diagnostyka

View full text Add to dashboard Cite

Heart disease is the leading cause of death in the world according to the World Health Organization (WHO). Researchers are more interested in using machine learning techniques to help medical staff diagnose or detect heart disease early. In this paper, we propose an efficient medical decision support system based on twin support vector machines (Twin-SVM) for heart disease diagnosing with binary target (i.e. presence or absence of disease). Unlike conventional support vector machines (SVM) that finds only one optimal hyperplane for separating the data points of first class from those of second class, which causes inaccurate decision, Twin-SVM finds two non-parallel hyper-planes so that each one is closer to the first class and is as far from the second class as possible. Our experiments are conducted on real heart disease dataset and many evaluation metrics have been considered to evaluate the performance of the proposed method. Furthermore, a comparison between the proposed method and several well-known classifiers as well as the state-of-the-art methods has been performed. The obtained results proved that our proposed method based on Twin-SVM technique gives promising performances better than the state-of-the-art. This improvement can seriously reduce time, materials, and labor in healthcare services while increasing the final decision accuracy.

show abstract

“…Therefore, the Gini coefficient or information entropy is also needed to evaluate the importance of features. This experiment ranks the importance of all features by using the feature importance method in Python's Sklearn library [ 27 ]. The feature importance method ranks the features according to the number of Gini coefficient drops.…”

Section: Experiments Designmentioning

confidence: 99%

Analyzing Surgical Treatment of Intestinal Obstruction in Children with Artificial Intelligence

Qiu

Chen

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Intestinal obstruction is a common surgical emergency in children. However, it is challenging to seek appropriate treatment for childhood ileus since many diagnostic measures suitable for adults are not applicable to children. The rapid development of machine learning has spurred much interest in its application to medical imaging problems but little in medical text mining. In this paper, a two-layer model based on text data such as routine blood count and urine tests is proposed to provide guidance on the diagnosis and assist in clinical decision-making. The samples of this study were 526 children with intestinal obstruction. Firstly, the samples were divided into two groups according to whether they had intestinal obstruction surgery, and then, the surgery group was divided into two groups according to whether the intestinal tube was necrotic. Specifically, we combined 63 physiological indexes of each child with their corresponding label and fed them into a deep learning neural network which contains multiple fully connected layers. Subsequently, the corresponding value was obtained by activation function. The 5-fold cross-validation was performed in the first layer and demonstrated a mean accuracy (Acc) of 80.04%, and the corresponding sensitivity (Se), specificity (Sp), and MCC were 67.48%, 87.46%, and 0.57, respectively. Additionally, the second layer can also reach an accuracy of 70.4%. This study shows that the proposed algorithm has direct meaning to processing of clinical text data of childhood ileus.

show abstract

Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction

Cited by 55 publications

References 27 publications

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

Efficient heart disease diagnosis based on twin support vector machine

Analyzing Surgical Treatment of Intestinal Obstruction in Children with Artificial Intelligence

Contact Info

Product

Resources

About