In general, doctors report that various types of diseases are caused to humans due to physical inactivity, especially due to obesity; there are many risks from minor damage to physical diseases that cause excessive damage. Due to this, doctors are giving advice to exercise to reduce obesity. But sustained high-intensity exercise can cause motor neuron disease in genetically predisposed individuals. 1 in 300 people who are heavy sleepers are likely to develop this disease. The disease affects the ability to walk, move and breathe as the motor neurons that carry messages from the brain to the muscles malfunction. It also shortens one's lifespan. It is difficult to understand who will be affected by what. People born with genetic risks and other environmental factors are factors for this disease. In this paper a neuro machine learning model was introduced to identify the of motor neuron disease and predicts the health impacts of this disease. The machine learning algorithm predicts the impacts of the impacts of motor neuron disease based on the different symptoms. In the saturation point the proposed model predicts 92.12% of Amyotopic Lateral Sclerosis, 93.28% of bulbar palsy, 91.44% of tendon erosion and 93.22% of polytopic paralysis based on the provided symptoms.

physical inactivity, high-intensity, motor neuron disease, heavy sleepers, brain, genetic risks, neuro machine learning model, Amyotopic Lateral Sclerosis, paralysis