Alzheimer’s disease has been one of the major concerns recently. Around 45 million people are suffering from this disease. Alzheimer’s is a degenerative brain disease with an unspecified cause and pathogenesis which primarily affects older people. The main cause of Alzheimer’s disease is Dementia, which progressively damages the brain cells. People lost their thinking ability, reading ability, and many more from this disease. A machine learning system can reduce this problem by predicting the disease. The main aim is to recognize Dementia among various patients. This paper represents the result and analysis regarding detecting Dementia from various machine learning models. The Open Access Series of Imaging Studies (OASIS) dataset has been used for the development of the system. The dataset is small, but it has some significant values. The dataset has been analyzed and applied in several machine learning models. Support vector machine, logistic regression, decision tree, and random forest have been used for prediction. First, the system has been run without fine-tuning and then with fine-tuning. Comparing the results, it is found that the support vector machine provides the best results among the models. It has the best accuracy in detecting Dementia among numerous patients. The system is simple and can easily help people by detecting Dementia among them.

In the Covid-19 pandemic, people have been very concerned about the safety and are avoiding crowded places like hospitals. An online telemedicine web-based technology can help to overcome this situation. This paper presents an online telemedicine system that helps to promote collaboration between doctors, hospitals, and patients. The system allows doctors to serve patients from remote areas. The system also allows both doctors and patients to communicate through video calls or text messages. Patients using the system can store information about their health, search for doctors, and consult medical professionals using text messages and video calls. Doctors can also register to serve patients, but they must ensure authenticity through registration. Doctors can write blogs, provide prescriptions, and can view the medical history of the patient. Hospitals can assign doctors in the respective departments. The system is tested in a lab environment. The system is fast and reliable with a user-friendly interface, enhancing telemedicine services countrywide for people with online access.