Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications.

Brain tumor identification and classification have improved due to the quick development of medical imaging and machine learning technology. This paper presents two approaches to secure image transmission in the Internet of Things (IoT): a comprehensive approach for brain tumor prediction and classification using a strong IoT infrastructure with cutting-edge machine learning models and a security approach with the implementation of the AES-ECC hybrid model in the MQTT communication protocol for image data encryption and decryption. We make use of a heterogeneous dataset that we sourced from the Kaggle Dataset platform, which includes four different types of MRI scans of brain tumors from 2870 patients. Our proposed methodology starts with the safe acquisition and transfer of MRI images through an IoT protocol infrastructure to a cloud-based platform. CNN, DenseNet, ResNet and G-Net are some of the sophisticated machine learning models that are used to interpret and analyse these pictures. The computer is trained to identify photos of brain tumors into the appropriate groups using all above four models. According to the data, our suggested CNN model performs better than the others, obtaining an amazing 89% accuracy rate. Nonetheless, we want to achieve even greater improvement in forecast precision by utilising ensemble boosting methodologies. Boosting the CNN model with Ada-Boost, Gradient Boost, XG Boost and Cat Boost algorithms aims to maximize prediction performance. We find that the CNN algorithm combined with XG Boost outperforms all other ensemble methods with an amazing accuracy rate of 97%. This encouraging result highlights how combining cutting-edge machine learning algorithms with IoT infrastructure can lead to better brain tumor classification and prognosis. The creation of more precise and effective diagnostic instruments for the identification of brain tumors is one of our study's many implications, one that will ultimately improve patient outcomes and the healthcare industry.