The interaction between abiotic and biotic parameters in an ecosystem usually shows health and functioning of the system. Thus, some physico-chemical parameters, phytoplankton abundance, chlorophyll a and primary production of the mangrove estuary in Sarawak, Malaysia were extensively investigated from January 2013 to December 2013 in order to establish the inter-linkage among them. The Pearson correlation coefficient revealed a significant relation between atmospheric and water temperatures (r = 0.692). Similarly, surface water temperature showed a significant positive correlation with salinity (r = 0.744), TDS (r = 0.708) and conductivity (r = 0.776). The light extinction coefficient (LEC, K) changed negatively in relation to TDS (r =-0.623), conductivity (r =-0.644) and surface water temperature (r =-0.766). Ammonium showed a negative correlation with rainfall (r =-0.620) but a positive correlation with salinity (r = 0.600). The biological variable such as phytoplankton abundance was found to be positively correlated with chlorophyll a (r = 0.692), ammonium (r = 0.645) and silica (r = 0.644) and negatively with rainfall (r =-0.644). The canonical correspondence analysis revealed a strong positive correlation between environmental parameters and phytoplankton species. The analysis of variance disclosed significant seasonal differences in salinity, water temperature, TDS, conductivity, LEC, ammonium and chlorophyll a.
Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.
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