Automatic Diagnosis Metabolic Syndrome via a $$k-$$ Nearest Neighbour Classifier

Behadada, Omar; Abi-Ayad, Meryem; Kontonatsios, Georgios; Trovati, Marcello

doi:10.1007/978-3-319-57186-7_45

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…which in agreement with the results of Belaïd et al (2012). The increase in blood glucose concentration is probably the result of glycogen degradation (glycogenolysis at the hepatic level), the hyperglycemia would be directly related to the adverse effects of aluminum on the pancreas and more exactly on insulin secretion by the islets of langerhans (Saka et al, 2011;Behadada, 2017). On the other hand, treatment with probiotic in AlCl3 intoxicated rats gave a significant decrease in blood glucose level in comparison with AlCl3 intoxicated rats (p<0.001).…”

Section: Blood Sugarsupporting

confidence: 88%

Prophylactic Effect of A Probiotic Strain Lactobacillus Rhamnosus Against Aluminum Chloride-Induced Intoxication in Wistar Rats: Neurobehavioral and Hematobiochemical Studies

Adli¹,

Benreguieg²,

Ziani³

et al. 2022

AGJSR

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Purpose: Aluminum (Al) and its various forms are environmental xenobiotics that pose great threats to public health. The probiotic strain has been reported to have important protective effects against aluminum cytotoxicity. Therefore, the current study was designed to assess the disruption of neurobehavioral and hematobiochemical parameters caused by chronic exposure to AlCl3, and then explore the protective effects of the probiotic strain Lactobacillus rhamnosus against these adverse in male rats. Method: Thirty-two animals were gathered into four groups: The first group served as control. The second group received 100mg AlCl3/kg bw for 12 weeks. However, the third group was administered 10mL probiotic strain /Kg (108 CFU/ml) bw/d daily for 4 weeks. Finally, group 4 received AlCl3 for 12 weeks, then the treatment was continued with probiotic for 4 weeks. Results: AlCl3 produced a decrease in body and brain weights with respect to their control. The results obtained also showed significant depression and anxiety-like behavior in the intoxicated animals, with a disturbance of the tested hematobiochemical parameters, particularly in renal and hepatic biomarkers. Overall, probiotics prevented enduring AlCl3-induced depression-like behavior in intoxicated rats. The results of the hematobiochemical, renal and hepatic analyses show a correction of the values after the administration of probiotics compared to those of intoxicated and untreated animals. Conclusion: The probiotic lactobacilli would be an alternative to reduce AlCl3 toxicity.

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Section: Blood Sugarsupporting

confidence: 88%

Prophylactic Effect of A Probiotic Strain Lactobacillus Rhamnosus Against Aluminum Chloride-Induced Intoxication in Wistar Rats: Neurobehavioral and Hematobiochemical Studies

Adli¹,

Benreguieg²,

Ziani³

et al. 2022

AGJSR

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“…Behadada et al [30] applied a k-Nearest Neighbour (k-NN) for predicting the relation of metabolic syndrome with physiological parameters (age, BMI, level of glucose in the blood, etc.). The model has been compared with Naïve Bayes (NB) and Artificial Neural Network (ANN) based on sensitivity that indicated the performance of the K-NN Model achieved 100% fit to this type of data.…”

Section: Review Machine Learning Methods For Diabetes Predictionmentioning

confidence: 99%

Machine Learning Methods for Diabetes Prediction

Dzakiyullah¹,

Burhanuddin²,

Ikram³

et al. 2019

IJITEE

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Machine Learning is one of the methods used for task prediction. In the diabetic’s research field, the application of machine learning is emerging since the advantages of approximation on the prediction technique has significantly given insight for many health practitioners. Machine Learning is utilized in order to handle the uncontrollable risk factor by finding a relation between such a risk factor trough prediction. This study aims to review recent machine learning models that have been used in diabetes prediction with respect to the risk factors in order to prevent diabetes. This study compares the performance of the model by justified the accuracy as the baseline to evaluate the model. The result of this review shows that the Random Forest and Support Vector Machine are the most popular technique among researcher. Moreover, from this study, it can be seen that Type 2 Diabetes Mellitus (T2DM) has been a concern by researchers since the incidence of diabetes was increasing in worldwide today that happened from an uncontrollable risk factor

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“…Their ability to employ non-invasive features for prediction sets these models apart, eliminating the need for invasive testing procedures. Furthermore, the capability of ML to intricately analyze metabolic patterns significantly enhances the specificity and sensitivity of MetS diagnosis [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning

Paplomatas,

Rigas,

Sergounioti

et al. 2024

Preprint

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The increasing prevalence of Metabolic Syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of Metabolic Syndrome, thereby addressing a significant concern in public health.

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Automatic Diagnosis Metabolic Syndrome via a $$k-$$ Nearest Neighbour Classifier

Cited by 6 publications

References 17 publications

Prophylactic Effect of A Probiotic Strain Lactobacillus Rhamnosus Against Aluminum Chloride-Induced Intoxication in Wistar Rats: Neurobehavioral and Hematobiochemical Studies

Prophylactic Effect of A Probiotic Strain Lactobacillus Rhamnosus Against Aluminum Chloride-Induced Intoxication in Wistar Rats: Neurobehavioral and Hematobiochemical Studies

Machine Learning Methods for Diabetes Prediction

Enhancing Metabolic Syndrome Detection through Blood Tests Using Advanced Machine Learning

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