Heart failure is a chronic cardiac condition characterized by reduced supply of blood to the body due to impaired contractile properties of the muscles of the heart. Like any other cardiac disorder, heart failure is a serious ailment limiting the activities and curtailing the lifespan of the patient, most often resulting in death sooner or later. Detection of survival of patients with heart failure is the path to effective intervention and good prognosis in terms of both treatment and quality of life of the patient. Machine learning techniques can be critical in this regard since they can be used to predict the survival of patients with heart failure in advance, allowing patients to receive appropriate treatment. Hence, six supervised machine learning algorithms have been studied and applied to analyze a dataset of 299 individuals from the UCI Machine Learning Repository and predict their survivability from heart failure. Three distinct approaches have been followed using Decision Tree Classifier, Logistic Regression, Gaussian Naïve Bayes, Random Forest Classifier, K-Nearest Neighbors, and Support Vector Machine algorithms. Data scaling has been performed as a preprocessing step utilizing the standard and min–max scaling method. However, grid search cross-validation and random search cross-validation techniques have been employed to optimize the hyperparameters. Additionally, the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) data resampling technique are utilized, and the performances of all the approaches have been compared extensively. The experimental results clearly indicate that Random Forest Classifier (RFC) surpasses all other approaches with a test accuracy of 90% when used in combination with SMOTE-ENN and standard scaling technique. Therefore, this comprehensive investigation portrays a vivid visualization of the applicability and compatibility of different machine learning algorithms in such an imbalanced dataset and presents the role of the SMOTE-ENN algorithm and hyperparameter optimization for enhancing the performances of the machine learning algorithms.

No abstract

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.

Bifacial rooftop photovoltaic panels appear to be an excellent means of power generation in this era of urbanization, especially for land-limited countries like Bangladesh. This paper presents a software-based approach to design and simulate a bifacial solar-panel-based energy model on the rooftop of the North Hall of Residence of the Islamic University of Technology, Gazipur. This vertically mounted model investigates the feasibility and applicability of such an energy model in a university residence, situated in a load-shedding-prone area. Hence, three prominent software platforms, namely PVSOL, PVsyst and System Advisor Model (SAM), are brought into action and rigorous simulations are performed for three different orientations; promising outcomes are observed in terms of annual energy yield, bifacial gain (BG) and consumption coverage of the grid and PV model. The annual energy demand of the North Hall is ~444 733.5 kWh. The three orientations can generate annually 92 508.62, 94 643.48 and 86 758.94 kWh, respectively. Hence, it is evident that the proposed orientations can supply almost 19–21% of the site’s annual demand. Monthly BG analysis shows an overall increase in energy gain of 13%, 15.6% and 6% for Orientation-1, Orientation-2 and Orientation-3, respectively. A rigorous comparative analysis and deviation analysis among the software results has been accomplished to gain more insight into the feasibility of the proposed system. Thus, we have focused on a detailed software-based estimation of energy production for different orientations of the PV panels, considering several factors, which will provide prior knowledge and assessment before going for hardware implementation in the future.

No abstract