Alzheimer’s disease will affect more people with increases in the elderly population, as the elderly population of countries everywhere generally rises significantly. However, other factors such as regional climates, environmental conditions and even eating and drinking habits may trigger Alzheimer’s disease or affect the life quality of individuals already suffering from this disease. Today, the subject of biomedical engineering is being studied intensively by many researchers considering that it has the potential to produce solutions to various diseases such as Alzheimer’s caused by problems in molecule or cell communication. In this study, firstly, a molecular communication model with the potential to be used in the treatment and/or diagnosis of Alzheimer’s disease was proposed, and its results were analyzed with an artificial neural network model. Secondly, the ratio of people suffering from Alzheimer’s disease to the total population, along with data of educational status, income inequality, poverty threshold, and the number of the poor in Turkey were subjected to detailed distribution analysis by using the random forest model statistically. As a result of the study, it was determined that a higher income level was causally associated with a lower risk of Alzheimer’s disease.

Nano-networks focused on communication of nano-sized devices (nanomachines) are a new communication concept, which is known as Molecular Communication (MC) in the literature. In this study, on the contrary to the literature, a mobile MC model is proposed in a diffusion environment by using 5 bits because it is known that besides the molecules, which transport information between the transmitter and receiver, the transmitter and receiver parts of the biological cells are mobile in the blood or any other fluid media. In this study, both the transmitter and the receiver can be chosen as mobile and/or fixed for some specific duties, such as drug delivery systems. Their mobility values can also be regulated separately for the proposed mobile MC model. The proposed model is analyzed for the different situations of the transmitter and receiver (fixed and/or mobile) by considering the fraction of the received molecules. Finally, the number of bits, the time step, and the bit duration are analyzed to find the best MC model. It is concluded that when the receiver and the transmitter are mobile, the distance between them changes, and finally, this affects the probability of the received molecules at the receiver.