Medical patient data need to be published and made available to researchers so that they can use, analyse, and evaluate the data effectively. However, publishing medical patient data raises privacy concerns regarding protecting sensitive data while preserving the utility of the released data. The privacypreserving data publishing (PPDP) process attempts to keep public data useful without risking the medical patients' privacy. Through protection methods like perturbing, suppressing, or generalizing values, which lead to uncertainty in identity inference or sensitive value estimation, the PPDP aims to reduce the risks of patient data being disclosed and to preserve the potential use of published data. Although this method is helpful, information loss is inevitable when attempting to achieve a high level of privacy using protection methods. In addition, the privacy-preserving techniques may affect the use of data, resulting in imprecise or even impractical knowledge extraction. Thus, balancing privacy and utility in medical patient data is essential. This study proposed an innovative technique that used a hybrid protection method for utility enhancement while preserving medical patients' data privacy. The utilized technique could partition information horizontally and vertically, resulting in data being grouped into columns and equivalence classes. Then, the attributes assumed to be easily known by any attacker are determined by upper and lower protection levels (U P L and LP L). This work also depends on making the false matches and value swapping to make sure that the attribute disclosure is less likely to happen. The innovative technique makes data more useful. According to the results, the innovative technique delivers about 93.4% data utility when the percentage of exchange level is 5% using LP L and 95% using U P L with a 4.5K medical patient dataset. In conclusion, the innovative technique has minimized risk disclosure compared to other existing works.

Students in the age group of 18–25 years witnessed their first pandemic in the form of the coronavirus (COVID-19). This had negative effects on their mental health, ranging from mild to severe. These effects also affected their personal and professional lives. The purpose of this study is to measure the anxiety and depression levels in college students due to COVID-19, along with the significant factors that contribute to that effect. We use the benchmark Hospital Anxiety and Depression Scale [HADS] questionnaire, as the basis of an online survey to capture the anxiety and depression levels amongst college students in various disciplines and at different levels. This survey was circulated through social media networks such as Facebook, WhatsApp, and so on. The responses were collected and analysed using Linear Regression and Random Forest algorithms for understanding the significant factors leading to the anxiety and depression of college students between ages 20 and 24.

Students pursuing different professional courses at Higher education level, during 2021-22 saw the first-time occurrence of a pandemic in the form COVID-19 and their mental health was affected, impacting their personal and professional lives. Many works are available in literature to assess the severity levels of the students. However, the need of the hour is to identify these students 'early' so that they can be treated effectively. Predictive analytics, a part of machine learning (ML) provides the way to classify 'early' the students with respect to the severity levels in mental health and this helps the clinical psychologists. As a case study and a novel initiative, in this work engineering and medical course students were comparatively analysed as their course content is heavy and the evaluation process is much stricter than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details etc., along with anxiety and depression questions using the Hospital Anxiety and Depression Scale [HADS]. The responses acquired through social media networks are analysed using ML algorithms -Support vector machines (robust handling of Health information) and J48 Decision tree (DT) (interpretability / comprehensibility). Also, Random Forest is used to identify the predictors for anxiety and depression. The results show, Support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision, and 1.0 recall, followed by Decision tree J48 classifier with 96%. Collectively it is found that medical students are affected by anxiety and depression marginally higher when compared with engineering students. Furthermore, this study aims to provide recommendations to have mental health screening as a regular practise in educational institutions to identify the undetected students.