Applications of Datamining Techniques for Predicting the Post - Covid 19 Symptoms in Saudi Arabia, Jazan

  Background The entire world was combating COVID-19; however, a significant proportion of patients demonstrate the persistence of some COVID-19 symptoms, new symptom development, or exaggeration of pre-existing disease after a negative viral load. They are referred to as a post-COVID-19 syndrome. According to various researches, COVID-19 has a wide range of long-term effects on virtually all systems, including the respiratory, cardiovascular, gastrointestinal, neurological, mental, and dermatological systems. Finding the various symptoms of post-acute and chronic is critical since they might have a significant impact on the patients' everyday functioning. As a result, we aimed to distinguish the symptoms immediately after the initial phase in which the symptoms affected them for more than three weeks using data mining techniques. Methodology: Post-COVID conditions do not affect everyone the same way. They can cause various types and combinations of symptoms in different people. The purpose of this research is to analyse the complications of post covid-19 syndrome. The purpose of Data mining is for discovering the knowledge from vast amount of database. To classify the symptoms of post covid-19, data mining techniques is used. In this study, ranking method was used in preprocessing to select subset of attributes for strengthening the rate of accuracy of classifiers. The data were collected through Google form of 384 household of students from Public Health College in Jazan University. The WEKA open-source software is used for this research work under Windows7 environment. An experimental study is carried out using data mining technique such as J48 and Random Forest tree. The data records are classified as six categories such as General symptoms, Nervous symptoms, Respiratory symptoms, Heart symptoms, Digestive symptoms and normal. Result: The performances of classifiers are evaluated through the confusion matrix in terms of accuracy, time taken to build the Model and error rate. It has been concluded that Random Forest Tree gives better accuracy, minimum time taken to build the model and less error rate than the J48 classifier

A Rare Case of Associated Double Ilioinguinal Nerve and Double Iliohypogastric

Background: Anatomical variations represent an embryological and comparative background for medicine and biology in order to understand the morphological aspect of the human body and its related structures. The present report aimed to describe an unfamiliar variation in the intra-abdominal course of the left Ilioinguinal and Iliohypogastric nerves. Methodology: A 77 male cadavers with the age range between (24-69 years) which were involved during routine dissection in department of anatomy among several Sudanese faculties during the period from (2016 – 2018) after obtaining the ethical approvals. Results: An abnormal course of left Ilioinguinal and left Iliohypogastric nerves in the abdomen by emerging as two nerves from the lateral border of the left psoas major muscle was mostly noticed. Conclusion: Variations in the intra-abdominal course of Ilioinguinal and Iliohypogastric nerves should be known by the surgeons during various abdominal surgeries

Predicting The Type Of Sleep Disorders Using Data Mining Classification Techniques

A fundamental human need, sleep is important for both physical and mental wellbeing. Our brain needs sleep to work correctly. Numerous negative effects may result from inadequate sleep or sleep of low quality. Conditions called sleep disorders cause changes in how we sleep. Our general health, safety, and enjoyment of life may be impacted by a sleep disturbance. Lack of sleep can develop many health issues. Insomnia, sleep apnea, restless legs syndrome, narcolepsy, parasomnias, and hypersomnia are only a few examples of the various forms of sleep disorders. Recent studies says that the obstructive sleep apnea risk and symptoms among middle-aged Saudi men and women and found they that 3 of every 10 Saudi men and 4 of every 10 Saudi women are at high risk for obstructive sleep apnea[1]. A simple pre-coded questionnaire will be developed, and data is collected from 151household students from the Public Health College in Jazan. The questionnaire includes socio demographic factors, sleep symptoms and behavioural data. The data science is an interdisciplinary field which is used to extract the knowledge from huge data. Hence, it plays a vital role to predict the type of sleep disorder. This paper focuses on how Data Mining classification helps to analyze the sleep disorder dataset with Random tree and One R. These algorithms are implemented using Weka tool. As a result, the classifiers performance was evaluated based on factors like confusion matrix. In our research we found that the classifier Random tree is giving more accuracy, minimum time taken to construct model and less error rate than One R classifier

Development Of Nanoparticles Of The Poorly Soluble Anti-Diabetic Drug (Pioglitazone) Drug

Diabetes mellitus is a pervasive metabolic disorder that poses a substantial global health burden. The treatment of Type 2 Diabetes (T2D), characterized by insulin resistance and impaired glucose metabolism, often involves the use of oral anti-diabetic medications. The encapsulation of PGZ within nanoparticles provides an opportunity to achieve controlled drug release, targeted delivery, enhanced therapeutic outcomes and enhancing PGZ solubility lies in the potential for improved therapeutic efficacy, dose reduction, enhanced patient compliance, and targeted drug delivery. The objective of this study was to prepare and evaluate poorly soluble anti-diabetic pioglitazone nanoparticles using tween 80, poloxamer 188 by emulsification technique. Pioglitazone is the first-line drug for the treatment of type II diabetes mellitus belongs to Biopharmaceutical Classification System Class II. The practically water-insoluble PGZ was nanoground by using nanosuspension by precipitation method. Tween 80 Emulsifier surface active agents were tested for their stabilizing effects. Different concentration of surfactant such as Tween 80 and ethanol: distilled water aqueous phase combination of surfactant was used for preparation of PGZ nanosuspension. The mean particle diameter of prepared nanoparticles ranged from 98.25to 150.55µm, zeta potential ranged from 22.13 to 30.60mV. The Yield Percentage (%) of these nanoparticles varies between 77.01 to 92.43%. The nanoparticles of PGZ Swelling Index were found to be in the range 0.855 to 2.887 and was % Drug loading 82.16 to 95.61 respectively. The results of evaluation analysis showed that particle size measurement, zeta potential and Yield Percentage. The outcomes of this research hold promise for revolutionizing PGZ therapy, offering an avenue to enhance its solubility and bioavailability, and ultimately improve patient outcomes and quality of life. The results of this study confirmed the sustained drug release profile of PGZ nanoparticles