Flood Prediction using MLP, CATBOOST and Extra-Tree Classifier

Flooding can be one of the many devastating natural catastrophes, resulting in the annihilation of life and damaging property. Additionally, it can harm farmland and kill growing crops and trees. Nowadays, rivers and lakes are being destroyed, and the natural water reservoirs are converted into development sites and buildings. Due to this, even just a bit of rain can cause a flood. To minimize the number of fatalities, property losses, and other flood-related issues, an early flood forecast is necessary. Therefore, machine learning methods can be used for the prediction of floods.To forecast the frequency of floods brought on by rainfall, a forecasting system is built using rainfall data. The dataset is trained using various techniques like the MLP classifier, the CatBoost classifier, and the Extra-Tree classifier to predict the occurrence of floods. Finally, the three models' performances are compared and the best model for flood prediction is presented. The MLP, Extra-Tree, and CatBoost models achieved accuracy of 94.5%, 97.9%, and 98.34%, respectively, and it is observed that CatBoost performed well with high accuracy to predict the occurrence of floods

Starting from scratch: building a new curriculum for faculty development program in emergency medicine by repurposing from a systemic review

Aim of the study: As we move towards globalization, health care professionals may find themselves working in a healthcare system that has a different patient population and disease epidemiology than their training. This study aims to develop a curriculum for a faculty development program for emergency medicine health care professionals in a private hospital in Kuwait who find themselves in such a situation. Material and methods: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, the authors systematically searched PubMed, CINAHL and ERIC from the inception of the database until June 2018, for search terms that would capture curriculum development for faculty development programs in emergency medicine or trauma. Two independent reviewers for relevance reviewed abstracts; included studies were retrieved for full-text analysis. A curriculum was developed using the topics requested by the needs assessment using the recommendations from the systematic review. Results: A total of 92 papers meeting the search criteria were identified of which 5 were included in the analysis. All 5 articles had education as the main objective of the curriculum for the faculty development program. All 5 articles had a faculty development program that was in the classroom setting. Four articles (80%) included a target audience of senior staff. Four articles (80%) recommended mentoring as an effective method for faculty development. Conclusions: The most effective method of the faculty development program was through mentorship. Further research is needed to dictate faculty development focusing on non-educational objectives

Nonbacterial Thrombotic Endocarditis: Presentation, Pathophysiology, Diagnosis and Management.

Initially described in 1936, non-bacterial thrombotic endocarditis (NBTE) is a rare entity involving sterile vegetations on cardiac valves. These vegetations are usually small and friable, typically associated with hypercoagulable states of malignancy and inflammatory diseases such as systemic lupus erythematosus. Diagnosis remains challenging and is commonly made post-mortem although standard clinical methods such as echocardiography (transthoracic and transesophageal) and magnetic resonance imaging may yield the clinical diagnosis. Prognosis of NBTE is poor with very high morbidity and mortality usually related to the serious underlying conditions and high rates of systemic embolization. Therapeutic anticoagulation with unfractionated heparin has been described as useful for short term prevention of recurrent embolic events in patients with NBTE but there are no guidelines for management of this disease

Classification and treatment of different stages of alzheimer’s disease using various machine learning methods

There has been a steady rise in the number of patients suffering from Alzheimer’s disease (AD) all over the world. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. The patient’s records are collected from National Institute on Aging, USA. The Sample consisted of initial visits of 496 subjects seen either as control or as patients. Patients were concerned about their memory at the National Institute on Aging. It also consisted of patients and caregiver interviews. This research work presents different models for the classification of different stages of Alzheimer’s disease using various machine learning methods such as Neural Networks, Multilayer Perceptron, Bagging, Decision tree, CANFIS and Genetic algorithms. The classification accuracy for CANFIS was found to be 99.55% which was found to be better when compared to other classification methods. Based on the outcome of classification accuracies, various management and treatment strategies such as pharmacotherapeutic and non pharmacotherapeutic interventions for mild, moderate and severe AD were elucidated, which can be of enormous use for the medical professionals in diagnosis and treatment of AD

Context-oriented user-centric search system for the IoT based on fuzzy clustering

The Internet of Things (IoT) paradigm envisions to support the creation of several applications that aids in the betterment of the society from various sectors such as environment, finance, industry etc. These applications are to be user-centric for their larger acceptance by the society. With the increase in the number of sensors that should are getting connected to the IoT infrastructure, there is an augmented increase in the amount of data generated by these sensors. Therefore it becomes a fundamental requirement to search for the sensors that produce the most applicable data required by the application. In this regard, context parameters of the sensors and the application users can be utilized to effectively filter out sensors from a large group. This paper proposes a sensor search scheme based on semantic-weights and fuzzy clustering. We have modified the traditional fuzzy c-means clustering algorithm by

Management of Hypertension: JNC 8 and Beyond

Hypertension is a leading risk factor for cardiovascular disease, the leading cause of death and morbidity in our society and on a global scale. Major components of cardiovascular disease include stroke, coronary artery disease, heart failure, and chronic kidney disease, in all of which hypertension plays a major role. The risk of these complications increases directly and linearly with systolic blood pressure starting at 115 mmHg. Although usually asymptomatic, hypertension is readily detectable on physical examination and is amenable to both lifestyle modification and pharmacologic treatment in most patients. However, large proportions of the hypertensive population remain undetected and undertreated. Numerous guidelines have been issued during the past few decades to promote detection and optimal therapy. Despite the increase in risk with systolic blood pressure greater than 115 mmHg, the generally accepted threshold for diagnosis and treatment has been systolic blood pressure greater than 139 mmHg and diastolic blood pressure greater than 80 mmHg because until recently treatment to lower levels has been associated with an unfavorable relation between clinical benefit and harm. In the past several years, new guidelines, advisories, commentaries, and clinical trials have provided evidence for a potential change in current recommendations for the management of hypertension. In this regard, the long-awaited eighth report of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure recommended patients older than 60 years be treated to a systolic blood pressure of less than 150 mmHg, which has generated considerable controversy and caution. The striking findings of the Systolic Blood Pressure Intervention Trial (SPRINT) have received considerable attention because of the demonstration that intensive therapy to a target systolic blood pressure below 120 mmHg decreases cardiovascular mortality and morbidity more than less intensive treatment to a target systolic blood pressure below 140 mmHg, but this approach is not fully generalizable because the trial excluded patients younger than 50 years and those with diabetes and prior stroke. This article addresses major issues in the management of hypertension, including those in the seventh and eight reports of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure and subsequent studies, considering maintenance of prior standards as well as the potential application of important new findings

A 70-Year-Old Man With Relapsed CNS Lymphoma Has Incidental Finding of Right Atrial Mass.

A 70-year-old man was admitted to the hospital for planned chemotherapy for recently diagnosed CNS lymphoma. His medical history included follicular lymphoma (achieved remission 1 year prior with chemotherapy) and tonic-clonic seizure 1 month prior to admission, which led to his eventual biopsy-confirmed diagnosis of CNS lymphoma. Physical examination revealed temperature 36.4 °C, heart rate of 60 beats/min, BP of 160/81 mm Hg, and 98% oxygen saturation on room air. Neurologic condition, including mental status examination, was normal. His cardiac examination revealed regular rate and rhythm with normal first and second heart sounds without murmurs, rubs, or gallops. The remainder of the examination was unremarkable. Review of systems noted progressive and intermittent confusion prior to his seizure. He denied any shortness of breath, dyspnea on exertion, orthopnea, lower extremity edema, palpitations, or syncope. Laboratory data were unremarkable

Classification of neurodegenerative disorders based on major risk factors employing machine learning techniques

Medical data mining has great potential forexploring the hidden patterns in the data sets of the medicaldomain. These patterns can be utilized for the classification ofvarious diseases. Data mining technology provides auser-oriented approach to novel and hidden patterns in the data. The present study consisted of records of 746 patients collectedfrom ADRC, ISTAART, USA. Around eight hundred andninety patients were recruited to ADRC and diagnosed forAlzheimer's disease (65%), vascular dementia (38%) andParkinson's disease (40%), according to the established criteria. In our study we concentrated particularly on the major riskfactors which are responsible for Alzheimer's disease, vasculardementia and Parkinson's disease. This paper proposes a newmodel for the classification of Alzheimer's disease, vasculardisease and Parkinson's disease by considering the mostinfluencing risk factors. The main focus