Metrics are used to help a software engineer in quantitative analysis to assess the quality of the design before a system is built. Object-Oriented (OO) design is becoming more popular in software development environment and OO design metrics are essential parts of software environment. The primary purpose of this paper is to analytically evaluate against the Weyuker's property and empirically validate, a proposed inheritance metrics (against a three versions of the same project) that can be used to measure the quality (especially focus on the quality factors "Reuse" and "Design Complexity") of an OO systems in terms of the using class inheritance tree.

A web application designed to predict high-risk patients affected by COVID-19 runs a machine learning model at the backend to generate results. The random forest classification technique is used to predict the high-risk status of patients who are COVID-19 positive and are at the initial stage of infection. We used hybridized algorithms to predict high-risk patients, and the model used the patients’ current underlying health conditions, such as age, sex, diabetes, asthma, hypertension, smoking, and other factors. After data preprocessing and training, the model could predict the severity of the patient with an accuracy of 65-70%. According to some studies, random forest ML models outperform other ML models for solving the challenge of predicting unusual events, such as in this case. Pneumonia, hypertension, diabetes, obesity, and chronic renal disease were the most contributory variables for model implementation. This project will help patients and hospital staff make necessary decisions and actions in advance. This will help healthcare workers arrange resources and hospital areas for high-risk COVID-19 patients. Thus, this study provides an effective and optimized treatment. Using this application and suitable patient data, hospitals can predict whether a patient will require urgent care.

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