DEVELOPMENT OF AN ARTIFICIAL INTELLIGENCE MODEL FOR THE EARLY DIAGNOSIS OF ALZHEIMER'S DISEASE

Abstract

The subject of the research is the development and evaluation of an artificial intelligence model for the early diagnosis of Alzheimer's disease. The novelty is the use of artificial intelligence methods, in particular machine learning algorithms, to develop a predictive model for diagnosing Alzheimer's disease at an early stage. Method. The proposed method includes several steps. First, a large data set consisting of clinical data (such as magnetic resonance imaging) is collected. This dataset serves as training data for the AI model. Machine learning algorithms are then applied to this data set to train the AI model to recognize patterns and identify signs that point to Alzheimer's disease. These algorithms learn from the data by iteratively adjusting their parameters until they can accurately classify Alzheimer's patients or healthy individuals. After training, the AI model is evaluated using a separate dataset that was not used during training. This score helps evaluate the performance of the model in terms of sensitivity (the ability to correctly identify Alzheimer's patients) and specificity (the ability to correctly identify healthy individuals). Method. The proposed method includes several steps. First, a large data set consisting of clinical data (such as magnetic resonance imaging) is collected. This dataset serves as training data for the AI model. Machine learning algorithms are then applied to this data set to train the AI model to recognize patterns and identify signs that point to Alzheimer's disease. These algorithms learn from the data by iteratively adjusting their parameters until they can accurately classify Alzheimer's patients or healthy individuals. After training, the AI model is evaluated using a separate dataset that was not used during training. This score helps evaluate the performance of the model in terms of sensitivity (the ability to correctly identify Alzheimer's patients) and specificity (the ability to correctly identify healthy individuals). Main results. The developed artificial intelligence model has achieved high accuracy in diagnosing Alzheimer's disease at an early stage. The model has shown promising performance in distinguishing between people with and without Alzheimer's disease based on MRI data. With further research and advances in AI technology, it is hoped that this model will be integrated into routine clinical practice, enabling early identification and action for people at risk of developing this devastating disease. Practical significance. Early detection of Alzheimer's disease is difficult due to subtle symptoms in the initial stages. However, with the development of artificial intelligence technologies, it is becoming possible to identify subtle patterns or biomarkers that may indicate early signs of Alzheimer's before significant cognitive decline occurs. Early detection is critical for effective treatment and disease control. By accurately identifying those at risk or in the early stages, healthcare professionals can intervene earlier, potentially improving patient outcomes. The development and evaluation of such AI models could lead to more efficient and accurate diagnosis, leading to improved patient care and potentially lower health care costs associated with late-stage diagnoses.