Comparison of Statistical Logistic Regression and RandomForest Machine Learning Techniques in Predicting Diabetes

Daghistani, Tahani; Alshammari, Riyad

doi:10.12720/jait.11.2.78-83

Cited by 53 publications

(15 citation statements)

References 17 publications

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“…CatBoost is commonly utilized in the fields of business (60), financial assessments (61), Medicare fraud detection (62), environmental science (63,64), and public science (36). According to our review of the literature, in the field of medicine, the random forest model has retained a competitive edge and is often superior in the prediction and classification of medical conditions compared with traditional logistic regression methods and machine learning methods such as neural networks, SVMs, and decision trees (65)(66)(67)(68). In our study, the CatBoost model outperformed the random forest model in classifying nonrecurrent and recurrent stroke.…”

Section: Discussionmentioning

confidence: 99%

Applying Machine Learning to Carotid Sonographic Features for Recurrent Stroke in Patients With Acute Stroke

Lin

Law

Yeh

et al. 2022

Front. Cardiovasc. Med.

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BackgroundAlthough carotid sonographic features have been used as predictors of recurrent stroke, few large-scale studies have explored the use of machine learning analysis of carotid sonographic features for the prediction of recurrent stroke.MethodsWe retrospectively collected electronic medical records of enrolled patients from the data warehouse of China Medical University Hospital, a tertiary medical center in central Taiwan, from January 2012 to November 2018. We included patients who underwent a documented carotid ultrasound within 30 days of experiencing an acute first stroke during the study period. We classified these participants into two groups: those with non-recurrent stroke (those who has not been diagnosed with acute stroke again during the study period) and those with recurrent stoke (those who has been diagnosed with acute stroke during the study period). A total of 1,235 carotid sonographic parameters were analyzed. Data on the patients' demographic characteristics and comorbidities were also collected. Python 3.7 was used as the programming language, and the scikit-learn toolkit was used to complete the derivation and verification of the machine learning methods.ResultsIn total, 2,411 patients were enrolled in this study, of whom 1,896 and 515 had non-recurrent and recurrent stroke, respectively. After extraction, 43 features of carotid sonography (36 carotid sonographic parameters and seven transcranial color Doppler sonographic parameter) were analyzed. For predicting recurrent stroke, CatBoost achieved the highest area under the curve (0.844, CIs 95% 0.824–0.868), followed by the Light Gradient Boosting Machine (0.832, CIs 95% 0.813–0.851), random forest (0.819, CIs 95% 0.802–0.846), support-vector machine (0.759, CIs 95% 0.739–0.781), logistic regression (0.781, CIs 95% 0.764–0.800), and decision tree (0.735, CIs 95% 0.717–0.755) models.ConclusionWhen using the CatBoost model, the top three features for predicting recurrent stroke were determined to be the use of anticoagulation medications, the use of NSAID medications, and the resistive index of the left subclavian artery. The CatBoost model demonstrated efficiency and achieved optimal performance in the predictive classification of non-recurrent and recurrent stroke.

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Section: Discussionmentioning

confidence: 99%

Applying Machine Learning to Carotid Sonographic Features for Recurrent Stroke in Patients With Acute Stroke

Lin

Law

Yeh

et al. 2022

Front. Cardiovasc. Med.

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“…We used the random forest model to make our diabetes risk assessment map, compared it with the assessment results of logistic regression, and noted that the assessment result was consistent with the actual prevalence. Thus, we conclude that the random forest model can achieve greater accuracy in assessing diabetes risk [ 41 ]. However, binary logistic regression analysis can intuitively explain diabetes risk factors, which is a disadvantage of the random forest model.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

Wang

Dong

Yang

2022

IJERPH

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Diabetes, which is a chronic disease with a high prevalence in people over 45 years old in China, is a public health issue of global concern. In order to explore the spatiotemporal patterns of diabetes among people over 45 years old in China, to find out diabetes risk factors, and to assess its risk, we used spatial autocorrelation, spatiotemporal cluster analysis, binary logistic regression, and a random forest model in this study. The results of the spatial autocorrelation analysis and the spatiotemporal clustering analysis showed that diabetes patients are mainly clustered near the Beijing–Tianjin–Hebei region, and that the prevalence of diabetes clusters is waning. Age, hypertension, dyslipidemia, and smoking history were all diabetes risk factors (p < 0.05), but the spatial heterogeneity of these factors was weak. Compared with the binary logistic regression model, the random forest model showed better accuracy in assessing diabetes risk. According to the assessment risk map generated by the random forest model, the northeast region and the Beijing–Tianjin–Hebei region are high-risk areas for diabetes.

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“…ROC: Receiver Operating Characteristic (ROC) Curve: It is a graphically way to display true positives versus false-positives across a series of cut-offs, and select the optimal cut off for clinical use. ( 13 )…”

Section: Methodsmentioning

confidence: 99%

Using Machine Learning to Predict Early Preparation of Pharmacy Prescriptions at PSMMC - a Comparison of Four Machine Learning Algorithms

Alhorishi¹,

Almeziny²,

Alshammari³

2021

Acta Inform Med

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Background: Patient satisfaction is one of the primary Key Performance Indicator (KPI) goal of health care service, and it creates many reasons for implementing research, plans, and innovations to achieve it for a better quality of life. Cutting Patient waiting time would increase patient satisfaction. Objective: A healthcare framework has been constructed utilizing a machine learning approach to construct an early predicting preparation model of pharmacy prescriptions and the worthiness of changing the outpatient pharmacy workflow. Methods: Data sets were retrieved between Januarys and June 2019 from Prince Sultan Military Medical City, Riyadh, KSA, for all patients who visited the clinics or discharged with pharmacy prescriptions. Included (1048575) instances and composed of (11) attributes. The evaluation criteria to compare the four algorithms were based on precision, Recall, True Positive Rate, False Negative Rate, F-measure, and Area under the curve. Results: Overall, 94.88% of patient’s shows at the pharmacy, female represents 58.89% of the data set while male represents 41.1%. RT gives the highest accuracy, with 97.22% in comparison to the other algorithms. Conclusion: The suggestion to change the pharmacy workflow is worth increasing patient satisfaction and overall the quality of the care.

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Comparison of Statistical Logistic Regression and RandomForest Machine Learning Techniques in Predicting Diabetes

Cited by 53 publications

References 17 publications

Applying Machine Learning to Carotid Sonographic Features for Recurrent Stroke in Patients With Acute Stroke

Applying Machine Learning to Carotid Sonographic Features for Recurrent Stroke in Patients With Acute Stroke

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

Using Machine Learning to Predict Early Preparation of Pharmacy Prescriptions at PSMMC - a Comparison of Four Machine Learning Algorithms

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