A Statistical Classifier to Support Diagnose Meningitis in Less Developed Areas of Brazil

Lélis, Viviane-Maria; Guzmán, Eduardo; Belmonte, María‐Victoria

doi:10.1007/s10916-017-0785-5

Cited by 13 publications

(15 citation statements)

References 17 publications

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“…This section summarises the construction process of our three decision models, described in detail in [42], [43]. Decision Model 1 (DM1) determines, only in terms of observable symptoms, whether or not the patient has meningitis; Decision Model 2 (DM2), using the same input symptoms, predicts the probability of having meningococcal meningitis; and finally, Decision Model 3 (DM3) explores the aetiology of the disease employing some chemical and cytological test data.…”

Section: ) Decision Models Constructionmentioning

confidence: 99%

Non-Invasive Meningitis Diagnosis Using Decision Trees

2020

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Meningitis is one of the pandemic diseases that many less developed countries suffer, primarily due to the lack of economic resources to face it. The more severe types of meningitis, Meningococcal Disease, MD, demand immediate medical attention since delays increase the risk of mortality. This paper presents an open and integrated Clinical Decision Support System to assist physicians in the different stages of meningitis diagnostics through observable symptoms. Our system integrates three intelligent components which try to give support to physicians in early diagnostics of meningitis. These components are based on interpretable tree-based machine learning models and knowledge-engineering techniques. A dataset of 26,228 records of patients with a meningitis diagnosis in Brazil was used to construct and evaluate the system. The performance indicators of the decision models exhibit an outstanding classification performance for MD meningitis with a classification accuracy of 94.3%. In order to test the correct diagnosis of the system, an evaluation study with real patients' data was performed. The experimental results concluded that excluding meningitis cases based only on observable symptoms is much more complicated than diagnosing it. However, the system properly diagnosed 88% of meningitis cases from the real database. INDEX TERMS Medical diagnosis, meningitis diagnostic models, tree-based machine learning.

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Section: ) Decision Models Constructionmentioning

confidence: 99%

Non-Invasive Meningitis Diagnosis Using Decision Trees

2020

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“…However, the traditional diabetes screening method needs an expensive blood test and extra manpower, which is a big challenge for the backward remote areas [ 8 ]. A diabetes screening model built by easily available indicators, without expensive examinations, is crucial to the occurrence and development of diseases [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

Xue

et al. 2020

Journal of Diabetes Research

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Background. An estimated 425 million people globally have diabetes, accounting for 12% of the world’s health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. Methods. A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. Results. The results indicated that XGBoost had the best performance (accuracy=0.906, precision=0.910, recall=0.902, F‐1=0.906, and AUC=0.968). The degree of variables’ importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving). Conclusions. We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables’ importance scores gives a clue to prevent diabetes occurrence.

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“…The main challenge in screening for diabetes is economic, including expensive blood work and additional human labor, which is even more challenging in developing countries [6]. The World Health Organization recommends that simple strategies should be developed to identify patients with risk for diabetes and then implement early lifestyle interventions [7].…”

Section: Introductionmentioning

confidence: 99%

“…Developing appropriate disease prediction algorithms can be technically challenging. In a Brazilian investigation, Lélis et al [6] applied seven classification techniques to make a diagnosis of meningococcal meningitis and demonstrated this model is accurate and affordable. Choi et al [8] developed two models to screen for prediabetes of 9251 individuals using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation, and concluded that the SVM model is superior to the ANN model in the screening for prediabetes.…”

Section: Introductionmentioning

confidence: 99%

Accurate and rapid screening model for potential diabetes mellitus

Pei

Gong

Kang

et al. 2019

BMC Med Inform Decis Mak

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BackgroundPrediction or early diagnosis of diabetes is crucial for populations with high risk of diabetes.MethodsIn this study, we assessed the ability of five popular classifiers (J48, AdaboostM1, SMO, Bayes Net, and Naïve Bayes) to identify individuals with diabetes based on nine non-invasive and easily obtained clinical features, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work stress, and salty food preference. A total of 4205 data entries were obtained from annual physical examination reports for adults in the Shengjing Hospital of China Medical University during January–April 2017. Weka data mining software was used to identify the best algorithm for diabetes classification.ResultsThe results indicate that decision tree classifier J48 has the best performance (accuracy = 0.9503, precision = 0.950, recall = 0.950, F-measure = 0.948, and AUC = 0.964). The decision tree structure shows that age is the most significant feature, followed by family history of diabetes, work stress, BMI, salty food preference, physical activity, hypertension, gender, and history of cardiovascular disease or stroke.ConclusionsOur study shows that decision tree analyses can be applied to screen individuals for early diabetes risk without the need for invasive tests. This procedure will be particularly useful in developing regions with high epidemiological risk and poor socioeconomic status, and enable clinical practitioners to rapidly screen patients for increased risk of diabetes. The key features in the tree structure could further facilitate diabetes prevention through targeted community interventions, which can potentially improve early diabetes diagnosis and reduce burdens on the healthcare system.

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A Statistical Classifier to Support Diagnose Meningitis in Less Developed Areas of Brazil

Cited by 13 publications

References 17 publications

Non-Invasive Meningitis Diagnosis Using Decision Trees

Non-Invasive Meningitis Diagnosis Using Decision Trees

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

Accurate and rapid screening model for potential diabetes mellitus

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