Environmental factors and machine learning for Alzheimer’s disease prediction

Background: One of the brain anomalies that typically affects the elderly is Alzheimer’s disease (AD) and its frequency has greatly grown during the previous few decades. AD is affected by many genetics and environmental circumstances. Environmental factors and the quantity of air pollutants are two of the most significant elements influencing the prevalence of AD. Methods: In this study, information from articles on the effects of air and environmental pollutants on AD was utilized. Additionally, the role of machine learning in predicting diseases was examined. Results: Several studies, approached from various perspectives, have delved into the factors influencing the onset of AD. The development of machine learning techniques has made it possible to record information about the environmental conditions and people’s habitats to make possible the occurrence of dementia-related abnormalities. According to the reviewed studies, certain biological pollutants can significantly increase the likelihood of developing AD. Also, it indicated the use of this technique has been based on biological information recorded for various diseases. The results showed that unhealthy environmental conditions increase the odds ratio of AD several times. Therefore, using this information provides the possibility to prevent the occurrence of AD. Conclusion: In general, reliable information on the living conditions of the elderly, together with other information about AD, allows for the accurate forecast needed to avert the loss of social and personal capital. The future contribution of this knowledge is something we can envision.