-The discharge of construction dewatering flows to the storm drainage network for disposal is a common activity in Qatar. The Dupuit empirical approach was utilized to establish various hypothetical dewatering scenarios on the basis of site classifications, which were modeled on 4 Case Study Areas of Doha's Existing Surface Drainage Network in order to study the impact of dewatering discharge against an established baseline. The simulations were undertaken using InfoWorks Integrated Catchment Modeling (ICM) software for critical and non-critical rainfall events. The results indicated significant localized flooding in excess of the baseline conditions for scenarios exceeding 0.5 m 3 /sec flows, while individual catchments demonstrated variations and sensitivities on the basis of catchment properties and rainfall events. It is evident that dewatering discharge under unpredictable rainfall events poses various levels of risk to the city's infrastructure, which is further exacerbated due to the massive scale of construction activity in the country and the rising ground water table in Greater Doha Area basin.

Abdominal liposuction is a commonly performed cosmetic procedure. However, as with any procedure, it can be associated with complications. One of the life-threatening complications of this procedure is visceral injury and bowel perforation. This complication is very rare, nevertheless general, and acute care surgeons must be aware of its possibility, its management, and its possible sequelae. We report a case of a 37-year-old female who underwent abdominal liposuction which was complicated by bowel perforation and was transferred to our facility for further care. She underwent an exploratory laparotomy in which multiple perforations were repaired. The patient then underwent multiple surgeries including stoma creation and had a long postoperative course. A literature review reveals the devastating sequelae of reported similar visceral and bowel injuries. The patient eventually did well and her stoma was reversed. This patient population will require close intensive care unit observation and a low threshold of suspicion for missed injuries during initial exploration. Further down the line, they will need psychosocial support and the mental health implications of this outcome must be cared for. The long-term aesthetic outcome is yet to be addressed.

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Machine learning in healthcare helps humans to process large and complex medical datasets and then analyze them into clinical insights which can help physicians in providing better medical care. Therefore, machine learning, when implemented in the medical field can lead to increased patient satisfaction. In this research, we will try to implement the functionalities of machine learning in healthcare in a single system. Health care can be made smart with the help of machine learning. Many cases can occur when the early diagnosis of an ailment is not within reach, So, their ailment prediction cannot be effectively implemented. As widely said “Prevention is better than cure”, prediction of diseases would lead to early prevention of occurrence of disease. Medical Staff are often overworked in the medical field and hence the diagnosis becomes prone to human errors and negligence. Patients should be given treatment and diagnosis that are accurate and precise. Mistreatment may result in worsening the condition of the patient and hence the need for precise diagnosis. Therefore, the application of machine learning in disease prediction is considered in this paper as the best practice to facilitate a better healthcare system and provide better treatment to a patient as soon as possible. This paper majorly focuses on the development of a web app that would work on symptoms collected from the user and medical data and store it in the system. This data then will be analyzed using different machine learning algorithms to deliver results with maximum accuracy. Keywords: Machine Learning, Random forest, Support Vector Machine, Supervised learning.

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