INTRODUCTION: Chronic Kidney Disease refers to the slow, progressive deterioration of kidney functions. However, the impairment is irreversible and imperceptible up until the disease reaches one of the later stages, demanding early detection and initiation of treatment in order to ensure a good prognosis and prolonged life. In this aspect, machine learning algorithms have proven to be promising, and points towards the future of disease diagnosis.OBJECTIVES: We aim to apply different machine learning algorithms for the purpose of assessing and comparing their accuracies and other performance parameters for the detection of chronic kidney disease. METHODS:The 'chronic kidney disease dataset' from the machine learning repository of University of California, Irvine, has been harnessed, and eight supervised machine learning models have been developed by utilizing the python programming language for the detection of the disease. RESULTS: A comparative analysis is portrayed among eight machine learning models by evaluating different performance parameters like accuracy, precision, sensitivity, F1 score and ROC-AUC. Among the models, Random Forest displayed the highest accuracy of 99.75%. CONCLUSION:We observed that machine learning algorithms can contribute significantly to the domain of predictive analysis of chronic kidney disease, and can assist in developing a robust computer-aided diagnosis system to aid the healthcare professionals in treating the patients properly and efficiently.

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Exam anxiety can be term as a mental disorder found in most students. It is a kind of fear and scaredness for which students choose to avoid the feared situations such as exams. A little bit of anxiety is common before and during the examination. Still, it can negatively impact their mental health and academic performances when it is more than the threshold level. The reasons might be expectations and pressure from parents and for competition with other peers. A cross-sectional study was conducted among medical students of Dhaka City in Bangladesh to see the status of examination phobia as they were usually going through lots of exam pressure than any other students. A structured questionnaire was used to conduct this study. WATS (West Side Test Anxiety Scale) was incorporated into the questionnaire to assess the phobia and anxiety levels of the students. Both descriptive and inferential statistics were performed for intensive analysis. Medical students must go through several different kinds of exams such as oral examination (viva), written, objective structured practical examination (OSPE), practical, short case, long case; all these things together play a contributing role in the induction of anxiety. More than 30% of students were suffering from moderate to severe examination anxiety, which compelled them to dropout or avoid the potential exams. The findings of this research can contribute significant impact on public health and mental health studies and the mental health professionals can provide policy guideline to the medical student to reduce exam anxiety. Further study needs to be done on a large scale to see a broad-spectrum scenario to assess the severity level of test anxiety and mental health status in the in the COVID-19 pandemic changing situation.

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