scholarly journals Machine Learning Techniques for Prediction of Early Childhood Obesity

2015 ◽  
Vol 06 (03) ◽  
pp. 506-520 ◽  
Author(s):  
S. Mukhopadhyay ◽  
A. Carroll ◽  
S. Downs ◽  
T. M. Dugan
Keyword(s):  
Machine Learning ◽  
Early Childhood ◽  
Decision Support ◽  
Childhood Obesity ◽  
Clinical Decision Support ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Early Childhood Obesity

Summary Objectives: This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Methods: Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Results: Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. Conclusions: This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two. Citation: Dugan TM, Mukhopadhyay S, Carroll AE, Downs SM. Machine learning techniques for prediction of early childhood obesity. Appl Clin Inform 2015; 6: 506–520http://dx.doi.org/10.4338/ACI-2015-03-RA-0036

Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

2019 ◽  
Vol 892 ◽  
pp. 274-283
Author(s):  
Mohammed Ashikur Rahman ◽  
Afidalina Tumian
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Cardiac Care ◽  
Learning Techniques

Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.

Diagnosing asthma and chronic obstructive pulmonary disease with machine learning

2017 ◽  
Vol 25 (3) ◽  
pp. 811-827 ◽  
Author(s):  
Dimitris Spathis ◽  
Panayiotis Vlamos
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Random Forest ◽  
Pulmonary Disease ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Forced Expiratory Volume ◽  
Random Forest Classifier ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease

This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease’s case, Random Forest classifier outperforms other techniques with 97.7 per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. In asthma’s case, the best precision, 80.3 per cent, is achieved again with the Random Forest classifier, while the most prominent attribute is MEF2575.

scholarly journals Development of machine learning-based clinical decision support system for hepatocellular carcinoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Gwang Hyeon Choi ◽  
Jihye Yun ◽  
Jonggi Choi ◽  
Danbi Lee ◽  
Ju Hyun Shim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Decision Support ◽  
Decision Support System ◽  
Treatment Option ◽  
Clinical Decision Support ◽  
Support System ◽  
Initial Treatment ◽  
Clinical Decision Support System ◽  
Clinical Decision

Abstract There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. We develop a machine learning-based clinical decision support system (CDSS) for recommending initial treatment option in HCC and predicting overall survival (OS). From hospital records of 1,021 consecutive patients with HCC treated at a single centre in Korea between January 2010 and October 2010, we collected information on 61 pretreatment variables, initial treatment, and survival status. Twenty pretreatment key variables were finally selected. We developed the CDSS from the derivation set (N = 813) using random forest method and validated it in the validation set (N = 208). Among the 1,021 patients (mean age: 56.9 years), 81.8% were male and 77.0% had positive hepatitis B BCLC stages 0, A, B, C, and D were observed in 13.4%, 26.0%, 18.0%, 36.6%, and 6.3% of patients, respectively. The six multi-step classifier model was developed for treatment decision in a hierarchical manner, and showed good performance with 81.0% of accuracy for radiofrequency ablation (RFA) or resection versus not, 88.4% for RFA versus resection, and 76.8% for TACE or not. We also developed seven survival prediction models for each treatment option. Our newly developed HCC-CDSS model showed good performance in terms of treatment recommendation and OS prediction and may be used as a guidance in deciding the initial treatment option for HCC.

scholarly journals A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185676-185687
Author(s):  
Noha Ossama El-Ganainy ◽  
Ilangko Balasingham ◽  
Per Steinar Halvorsen ◽  
Leiv Arne Rosseland
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Critical Care Units

scholarly journals Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

2021 ◽  
Vol 17 (11) ◽  
pp. 60
Author(s):  
Halima EL Hamdaoui ◽  
Said Boujraf ◽  
Nour El Houda Chaoui ◽  
Badr Alami ◽  
Mustapha Maaroufi
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Support ◽  
Random Forest ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Random Forest Algorithm ◽  
Adaboost Algorithm

heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to automated diagnosis. This paper aims to predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.

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