Introduction: Dyslipidemia has been noted to play an integral role in the pathogenesis and progression of micro and macrovascular complications in Diabetes Mellitus (DM) patients. Lipid profile is the indicators of dyslipidemia. Objectives: To evaluate the prevalence and pattern of dyslipidemia in type 2 DM patients. Materials and Methods: This cross sectional study was conducted at Armed Forces Institute of Pathology (AFIP) from November 2014 to October 2015. The study included 300 type 2 diabetic patients belonging to the age group 30-59 years. Personal data and history of co-existing medical conditions were collected by data collection sheet and analyzed. Results: Among 300 study subjects with type 2 DM the prevalence of dyslipidemia was 94% among them 19% had single dyslipidemia and 75% had multiple dyslipidemia. In this study, high level of total cholesterol (TC), triglycerides(TG) and Low Density Lipoprotein-Cholesterol (LDL-C) were found in 47.3%, 76.7% and 41.3% patients respectively. High Density Lipoprotein- Cholesterol (HDL-C) levels were found to be low in 60% patients. Conclusion: The study revealed that dyslipidemia is very common in type 2 diabetic patients and the most common abnormality observed was increased serum TG level followed by decreased HDL-C level. So, patients with type 2 DM should be followed up with serum lipid profile regularly. Journal of Armed Forces Medical College Bangladesh Vol.14 (2) 2018: 177-179
Cardiovascular diseases (CVDs) are prevalent disorders affecting the heart or blood arteries. Early disease detection significantly enhances survival prospects, thus emphasizing the necessity for accurate prediction methods. Emerging technologies, such as machine learning (ML), present promising avenues for more precise prediction of CVDs. However, a critical challenge lies in developing models that not only ensure optimal predictive performance but also conform to well-established domain knowledge, thereby enhancing their credibility. Single classifiers often fall short due to issues like overfitting and bias. In response, this study proposes a domain knowledge-based feature selection integrated with a stacking ensemble classifier. The Framingham Heart Study, UCI Heart Disease and UAE retrospective cohort study datasets were utilized for training and evaluation of the ML algorithms. The results indicate that the proposed domain knowledge-based feature selection performs on par with frequently adopted feature selection techniques. Moreover, the proposed stacked ensemble, in conjunction with domain knowledge-based feature selection, achieved the highest metrics with 89.66% accuracy, and 89.16% F1-score on the Framingham dataset. Similarly, the proposed method achieved an F1-score of 85.26% and 96.23% on the UCI Heart Disease and UAE datasets. Furthermore, this study employs explainable AI techniques to illuminate the decision-making process of the predictive models. Thus, the study establishes that domain knowledge-based feature selection promotes the credibility of ML models without compromising predictive performance.
Introduction: Anaemia is the commonest nutritional problem in the world but the burden of anaemia is disproportionately borne among children in developing countries. Physical, mental and social developments of the children are adversely affected by childhood anaemia. Objective: To assess the prevalence and severity of anaemia based on haemoglobin levels in children less than five years of age attending in Combined Military Hospital (CMH), Sylhet. Materials and Methods: This cross sectional study was conducted at CMH, Sylhet from January 2018 to December 2018. The study included 184 children aged 6 months to less than 5 years. Personal data and history of co-existing medical conditions were collected by data collection sheet and then analyzed. Results: The prevalence of anaemia was 74(40.2%). Out of the anaemic under five children, 46 (75.7%) had mild anaemia, 18(24.3%) had moderate anaemia, and no one had severe anaemia (WHO definition). There was no significant difference for prevalence of anaemia in relation to sex and age different groups. Conclusion: The prevalence of anaemia in 6 months to less than 5 years children was found to be high, given the negative impact of anaemia on the development of children in future, so there is an urgent need for effective and efficient remedial health interventions. JAFMC Bangladesh. Vol 16, No 2 (December) 2020: 76-79
Introduction:Atherogenic index of plasma (AIP) is defined as log of TG to HDL-C ratio. People with high AIP have a higher risk for coronary heart disease (CHD) than those with low AIP. AIP is useful in predicting atherogenecity. Objectives: To determination of AIP among the study subjects and find out the prevalence of AIP among type 2 diabetes mellitus (DM) patients. Materials and Methods: This cross sectional study was conducted at Armed Forces Institute of Pathology (AFIP) from November 2014 to October 2015. The study included 300 type 2 DM patients belonging to the age group 30-60 years. Fasting plasma glucose (FPG), HDL-C, TG were estimated. The AIP was calculated as log (TG/HDL-C) using the Czech online calculatorof atherogenic risk. Personal data and history of co-existing medical conditions were collected by data collection sheet. Data were analyzed by SPSS version 18.0. Results: Among 300 study subjects the AIP were found in the range of “increased risk” in 298(99.3%) and “low risk” in 02(0.7%). In this study mean FPG was 9.81±3.08 mmol/L and mean AIP was 0.73 ± 0.23A and significant positive correlation between FPG and AIP (r = 0.123, p < 0.05) was observed. Conclusion: The study revealed that AIP is significantly higher in type 2 DM patients. So, patients with type 2 DM should be followed up with AIP regularly. JAFMC Bangladesh. Vol 15, No 2 (December) 2019: 204-205
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