Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach

Yu, Cheng; Lin, Yu; Lin, Chang Hsien; Lin, Shiyng Yu; Wu, Jenny L; Chang, Shy Shin

doi:10.2196/18585

Cited by 32 publications

(24 citation statements)

References 55 publications

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“…Machine learning (ML) methods are most commonly used to construct medica database system from electronic health records for those patients who have undergone health examination [44]. These ML methods are subdivided into support vector machine methods, bayesian methods, and decision tree methods.…”

Section: B Machine Learning Methodsmentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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Electronic health records are used to extract patient's information instantly and remotely, which can help to keep track of patients' due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using 'HIPAA Safe Harbor' technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.

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Section: B Machine Learning Methodsmentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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“…Previous studies have shown the effectiveness and usefulness of web-based risk assessments in identifying risks and assisting the decision-making for pediatric readmission prediction [34], preventive medicine [35], violent behavior prediction [36], and trauma therapy [37]. Thus, in this section, we aim to design and develop web-based self-care prediction application (web-app) to provide decision tools for therapist in diagnosing children with disabilities.…”

Section: Practical Application Of the Proposed Self-care Prediction Mmentioning

confidence: 99%

“…As suggested by Demšar [33], a statistical significance test can be utilized to prove the significance of the proposed model as compared to other classification models. Furthermore, previous studies have also reported the effectiveness and usefulness of the practical application of prediction model to identify risks and assist the decision-making for pediatric readmission prediction [34], preventive medicine [35], violent behavior prediction [36], and trauma therapy [37].…”

Section: Introductionmentioning

confidence: 99%

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

et al. 2020

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Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.

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“…Machine learning is an artificial intelligence technique in which can the algorithm automatically learns and improves from experience or large amounts of data without being explicitly programmed. The kernel of machine learning is a statistical analysis that provides a powerful and purposeful method of observing specific patterns and correlations in health care issues by exploring undiscovered data, resulting in the establishment of data-driven prediction models (16)(17)(18)(19)(20)(21). Several clinical issuessuch as chronic kidney disease, postoperative sepsis, and alexithymia in fibromyalgia-have been explored using machine learning (22)(23)(24).…”

Section: Introductionmentioning

confidence: 99%

Identify the Characteristics of Metabolic Syndrome and Non-obese Phenotype: Data Visualization and a Machine Learning Approach

Chang

Lin

et al. 2021

Front. Med.

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Introduction: A third of the world's population is classified as having Metabolic Syndrome (MetS). Traditional diagnostic criteria for MetS are based on three or more of five components. However, the outcomes of patients with different combinations of specific metabolic components are undefined. It is challenging to be discovered and introduce treatment in advance for intervention, since the related research is still insufficient.Methods: This retrospective cohort study attempted to establish a method of visualizing metabolic components by using unsupervised machine learning and treemap technology to discover the relations between predicting factors and different metabolic components. Several supervised machine-learning models were used to explore significant predictors of MetS and to construct a powerful prediction model for preventive medicine.Results: The random forest had the best performance with accuracy and c-statistic of 0.947 and 0.921, respectively, and found that body mass index, glycated hemoglobin, and controlled attenuation parameter (CAP) score were the optimal primary predictors of MetS. In treemap, high triglyceride level plus high fasting blood glucose or large waist circumference group had higher CAP scores (>260) than other groups. Moreover, 32.2% of patients with high CAP scores during 3 years of follow-up had metabolic diseases are observed. This reveals that the CAP score may be used for detecting MetS, especially for the non-obese MetS phenotype.Conclusions: Machine learning and data visualization can illustrate the complicated relationships between metabolic components and potential risk factors for MetS.

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Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach

Cited by 32 publications

References 55 publications

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

Identify the Characteristics of Metabolic Syndrome and Non-obese Phenotype: Data Visualization and a Machine Learning Approach

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