首页|Nigerian Institute of Medical Research Reports Findings in Hyperglycemia (Machin e Learning-Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals)
Nigerian Institute of Medical Research Reports Findings in Hyperglycemia (Machin e Learning-Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Nutritional and Metabo lic Diseases and Conditions - Hyperglycemia is the subject of a report. Accordin g to news reporting from Lagos, Nigeria, by NewsRx journalists, research stated, “Noncommunicable diseases continue to pose a substantial health challenge globa lly, with hyperglycemia serving as a prominent indicator of diabetes. This study employed machine learning algorithms to predict hyperglycemia in a cohort of in dividuals who were asymptomatic and unraveled crucial predictors contributing to early risk identification.” The news correspondents obtained a quote from the research from the Nigerian Ins titute of Medical Research, “This dataset included an extensive array of clinica l and demographic data obtained from 195 adults who were asymptomatic and residi ng in a suburban community in Nigeria. The study conducted a thorough comparison of multiple machine learning algorithms to ascertain the most effective model f or predicting hyperglycemia. Moreover, we explored feature importance to pinpoin t correlates of high blood glucose levels within the cohort. Elevated blood pres sure and prehypertension were recorded in 8 (4.1 %) and 18 (9.2% ) of the 195 participants, respectively. A total of 41 (21%) partic ipants presented with hypertension, of which 34 (83%) were female. However, sex adjustment showed that 34 of 118 (28.8%) female partic ipants and 7 of 77 (9%) male participants had hypertension. Age-bas ed analysis revealed an inverse relationship between normotension and age (r=-0. 88; P=.02). Conversely, hypertension increased with age (r=0.53; P=.27), peaking between 50-59 years. Of the 195 participants, isolated systolic hypertension an d isolated diastolic hypertension were recorded in 16 (8.2%) and 15 (7.7%) participants, respectively, with female participants record ing a higher prevalence of isolated systolic hypertension (11/16, 69% ) and male participants reporting a higher prevalence of isolated diastolic hype rtension (11/15, 73 %). Following class rebalancing, the random fore st classifier gave the best performance (accuracy score 0.89; receiver operating characteristic-area under the curve score 0.89; F1-score 0.89) of the 26 model classifiers. The feature selection model identified uric acid and age as importa nt variables associated with hyperglycemia. The random forest classifier identif ied significant clinical correlates associated with hyperglycemia, offering valu able insights for the early detection of diabetes and informing the design and d eployment of therapeutic interventions.”
LagosNigeriaAfricaCardiovascular D iseases and ConditionsCyborgsEmerging TechnologiesGlucose Metabolism Disor dersHealth and MedicineHyperglycemiaHypertensionMachine LearningMetabo lic Diseases and ConditionsNutritional and Metabolic Diseases and ConditionsRisk and Prevention
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