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Tuberculosis is an infectious disease that spreads through the air when someone sneezes or coughs, and it mainly affects the lungs. It is a very common disease, prevailing mainly in undeveloped and developing nations. Inflammation of the lungs Tuberculosis can be diagnosed with the help of radiology by taking a chest X-ray of the patient and a sample of sputum to check whether bacteria (Mycobacterium) exist or not. The techniques of artificial intelligence can help in the diagnosis and fast predictions of tuberculosis. In this research paper, we analysed the dataset of tuberculosis patents by applying the classification models of machine learning. The accuracy of models will be compared by varying the training and testing datasets. The CPU execution time of the classification models is also calculated, to determine which model performs faster. [ABSTRACT FROM AUTHOR]

The pandemic COVID-19 generates the requirement of remote health monitoring tremendously. It changes the healthcare system and individual perception about health monumentally. Remote health monitoring will help in regular monitoring as well as keeping our health workers safe from different types of infections. Due to pandemic lots of advancement in healthcare system were done and generates the requirement for advances in intelligent health care systems. Therefore, storing data related to health that has been associated to electronic health records (EHR), Medical Chart, and others is increasing exponentially. The IoT with multiple sensor device minimizes the number of health-related problems and health ideologies, the cost of using data, and data analytics performed by connecting data streams using cloud computing. IoT devices enable users to reduce costs related to health monitoring and risk related to health, so that patient data can be collected and data analyzed to customize data flows using cloud computing. Patients' parameters such as body temperature, blood pressure (B.P.), heart rate, etc. are sensed by special sensors, which are then sent to the cloud database (DBaaS) storage for automated-disease prediction method based on parental parameters. With this method, both physicians and patients are given a quick medical assessment, and it is important that it is appropriate to use whenever and wherever. By gathering parameters required through monitoring the current state of patients from the cloud using IoT, patients can receive appropriate and effective medical treatment. In this proposed work, Cloud IoT is integrated with numerous techniques available for data analytics data tools and techniques in healthcare systems and various appropriate precision data analytics algorithms, developed in Cloud Internet of Things (IoT) locations. Finally, the contribution is recognized in resolving the state of assembly of all of these platforms as active health, which could contribute to a unified routine learning health system for the future. The advances in EHR technology will give new standards for those applications that use health monitoring and health prediction. Sensor data communicated to cloud storage where it is interpreted and act smartly upon complex information of patient to find accurate diagnosis, précised medication & a learning health system. [ABSTRACT FROM AUTHOR]