A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data.

By comparing the performance of various tree algorithms, we can determine which one is most useful for analyzing biomedical data. In artificial intelligence, decision trees are a classification model known for their visual aid in making decisions. WEKA software will evaluate biological data from real patients to see how well the decision tree classification algorithm performs. Another goal of this comparison is to assess whether or not decision trees can serve as an effective tool for medical diagnosis in general. In doing so, we will be able to see which algorithms are the most efficient and appropriate to use when delving into this data and arrive at an informed decision.

Processing Decision Tree Data Using Internet of Things (IoT) and Artificial Intelligence Technologies with Special Reference to Medical Application.

Alternative methods are available for a wide range of medical conditions. Idealistically, doctors would have a tool that would analyse their patients' symptoms and suggest the most accurate diagnosis and treatment plan. Artificial intelligence uses decision trees to predict and classify large datasets. A decision tree is a versatile prediction model. Its main purpose is to learn from observations and logic. Rule-based prediction systems represent and categorize events. We discuss the basic properties of decision trees and successful medical alternatives to the classic induction strategy. The study reviews some of the most important medical applications of decision trees (classification). We show researchers and managers how to accurately assess hospital and epidemic management behaviour. Additionally, we discuss decision trees and their applications. The results showed the effectiveness of decision trees in processing medical data by using internet of things (IoT) and artificial intelligence technologies in medical applications. Accordingly, the researchers recommend the use of these technologies in other fields of studies.

Machine Learning of ZnO Interaction with Immunoglobulins and Blood Proteins in Medicine.

Toxoplasmosis is a zoonotic illness caused by Toxoplasma gondii. Those with a normal immune system normally recover without treatment. Immunocompromised individuals and pregnant women must be treated regularly. Toxoplasmosis is a serious illness that may reactivate in immunocompromised patients. A retrospective study using machine learning of toxoplasmosis patients at Government Fever Hospital in Gorantla, Guntur, India, included 25 women, eight of whom were pregnant. These included sex, age, symptoms and side effects, pregnancy, ophthalmic, and antitoxoplasmosis titers, and treatment regimens. Protease mobility and specific activity were increased in toxoplasmosis-infected women's sera, although not significantly (p=0.05). However, there was no discernible decline. The impacts of nanoparticle impact demonstrated a 52.24 percent drop in compound concentration in the presence of zinc nanoparticles, whereas the effect of ZnO nanoparticles was 51.37 percent. Zinc nanoparticles lowered IgA, IgG, and IgM levels in the eye.

Studying the Effects of Cold Plasma Phosphorus Using Physiological and Digital Image Processing Techniques.

The goal of this study is to see how cold plasma affects rabbit bone tissue infected with osteoporosis. The search is divided into three categories: control, infected, and treated. The rabbits were subjected to cold plasma for five minutes in a room with a microwave plasma voltage of "175 V" and a gas flow of "2." A histopathological photograph of infected bone cells is obtained to demonstrate the influence of plasma on infected bone cells, as well as the extent of destruction and effect of plasma therapy before and after exposure. The findings of the search show that plasma has a clear impact on Ca and vitamin D levels. In the cold plasma, the levels of osteocalcin and alkali phosphates (ALP) respond as well. Image processing techniques (second-order gray level matrix) with textural elements are employed as an extra proof. The outcome gives good treatment indicators, and the image processing result corresponds to the biological result.

Advanced Deep Learning Human Herpes Virus 6 (HHV-6) Molecular Detection in Understanding Human Infertility.

To see if HHV-6 may be a cause of infertility, researchers looked at 18 men and 10 women who had unexplained critical fertility and had at least one prior pregnancy. HHV-6 DNA was discovered in both infertile and fertile peripheral blood mononuclear cells (PBMC) (12 and 14%, respectively); endometrial epithelial cells from 4/10 (40%) infertile women were positive for HHV-6 DNA; this viral DNA was not found in the endometrium of fertile women. When endometrial epithelial cells were cultivated, they produced viral early and late proteins, suggesting the existence of an infectious virus. Endometrial HHV-6 infection creates an aberrant NK cell and cytokine profile, resulting in a uterine domain that is not favorable to conception, according to the findings. To corroborate the findings, studies of extra fertile and barren women should be done. Semen samples were taken from 18 guys who visited the Government General Hospital Guntur's infertility department because they were having reproductive issues with their partners. Herpes virus DNA has been discovered in the sperm of symptomatic fertile and infertile male patients on rare instances. Furthermore, researchers must investigate the role of viral diseases in male infertility.

Psychological Impact of COVID-19 Pandemic in Bangladesh: Analysis of a Cross-Sectional Survey.

The COVID-19 pandemic has generated fear, panic, distress, anxiety, and depression among many people in Bangladesh. In this cross-sectional study, we examined factors associated with different levels of psychological impact as a result of COVID-19 in Bangladesh. From April 1 to 30, 2020, we used a self-administered online questionnaire to collect data from 10,609 respondents. Using the Impact of Event Scale-Revised to assess the psychological impact of the COVID-19 pandemic on respondents, we categorized the levels of impact as normal, mild, moderate, or severe. Ordinal logistic regression was used to examine the associated factors. The prevalence of mild, moderate, and severe psychological impact was 10.2%, 4.8%, and 45.5%, respectively. Multivariate analysis revealed that the odds of reporting normal vs mild, moderate, or severe psychological impact were 5.9 times higher for people living in the Chittagong Division, 1.7 times higher for women with lower education levels, 3.0 times higher among those who were divorced or separated, 1.8 times higher for those working full time, and 2.4 times higher for those living in shared apartments. The odds of reporting a psychological impact were also higher among people who did not enforce protective measures inside the home, those in self-quarantine, those who did not wear face masks, and those who did not comply with World Health Organization precautionary measures. Increased psychological health risks due to COVID-19 were significantly higher among people who experienced chills, headache, cough, breathing difficulties, dizziness, and sore throat before data collection. Our results showed that 1 in 2 respondents experienced a significant psychological impact as a result of the COVID-19 pandemic. Public health researchers should consider these factors when targeting interventions that would have a protective effect on the individual's psychological health during a pandemic or future disease outbreak.

Prevalence and Factors Associated with Mental Health Impact of COVID-19 Pandemic in Bangladesh: A Survey-Based Cross-Sectional Study.

Background: Feelings of isolation, insecurity, and instability triggered by COVID-19 could have a long-term impact on the mental health status of individuals. Objectives: The aim of this study was to examine the prevalence of mental health symptoms (anxiety, depression, and stress) in Bangladesh and the factors associated with these symptoms during the COVID-19 pandemic. Methods: From 1 to 30 April 2020, we used a validated self-administered questionnaire to conduct a cross-sectional study on 10,609 participants through an online survey platform. We assessed mental health status using the Depression, Anxiety, and Stress Scale (DASS-21). The total depression, anxiety, and stress subscale scores were divided into normal, mild, moderate, severe, and multinomial logistic regression was used to examine associated factors. Findings: The prevalence of depressive symptoms was 15%, 34%, and 15% for mild, moderate, and severe depressive symptoms, respectively. The prevalence of anxiety symptoms was 59% for severe anxiety symptoms, 14% for moderate anxiety symptoms, and 14% for mild anxiety symptoms, while the prevalence for stress levels were 16% for severe stress level, 22% for moderate stress level, and 13% for mild stress level. Multivariate analyses revealed that the most consistent factors associated with mild, moderate, and severe of the three mental health subscales (depression, anxiety, and stress) were respondents who lived in Dhaka and Rangpur division, females, those who self-quarantined in the previous seven days before the survey, and those respondents who experienced chills, breathing difficulty, dizziness, and sore throat. Conclusion: Our results showed that about 64%, 87%, and 61% of the respondents in Bangladesh reported high levels of depression, anxiety, and stress, respectively. There is a need for mental health support targeting women and those who self-quarantined or lived in Dhaka and Rangpur during the pandemic.