Abstract:Dementia is a brain disease that stays in the seventh position of death rate as per the report of the World Health Organization (WHO). Among the various types of dementia, Alzheimer’s disease has more than 70% of cases of dementia. The objective is to predict dementia disease from the open access series of imaging studies (OASIS) dataset using machine learning techniques. Also, the performance of the machine learning model is analyzed to improve the performance of the model using the cuckoo algorithm. In this … Show more
“…Consideration of this factor is critical, as the data collected may contain inherent errors. Consequently, strategies must be implemented to manage and reduce the margin of error associated with the sample, thus ensuring greater reliability in the inferences and results obtained during the statistical analysis [30], [31].…”
The increasing prevalence of Alzheimer's disease in older adults has raised significant concern in recent years. Aware of this challenge, this research set out to develop predictive models that allow early identification of people at risk for Alzheimer's disease, considering several variables associated with the disease. To achieve this objective, data mining techniques were employed, specifically the decision tree algorithm, using the RapidMiner Studio tool. The sample explore modify model and assess (SEMMA) methodology was implemented systematically at each stage of model development, ensuring an orderly and structured approach. The results obtained revealed that 45.00% of people with dementia present characteristics that identify them as candidates for confirmation of a diagnosis of Alzheimer's disease. In contrast, 52.78% of those who do not have dementia show no danger of contracting the disease. In the conclusion of the research, it was noted that most patients diagnosed with Alzheimer's are older than 65 years, indicating that this stage of life tends to trigger brain changes associated with the disease. This finding underscores the importance of considering age as a key factor in the early identification of the disease.
“…Consideration of this factor is critical, as the data collected may contain inherent errors. Consequently, strategies must be implemented to manage and reduce the margin of error associated with the sample, thus ensuring greater reliability in the inferences and results obtained during the statistical analysis [30], [31].…”
The increasing prevalence of Alzheimer's disease in older adults has raised significant concern in recent years. Aware of this challenge, this research set out to develop predictive models that allow early identification of people at risk for Alzheimer's disease, considering several variables associated with the disease. To achieve this objective, data mining techniques were employed, specifically the decision tree algorithm, using the RapidMiner Studio tool. The sample explore modify model and assess (SEMMA) methodology was implemented systematically at each stage of model development, ensuring an orderly and structured approach. The results obtained revealed that 45.00% of people with dementia present characteristics that identify them as candidates for confirmation of a diagnosis of Alzheimer's disease. In contrast, 52.78% of those who do not have dementia show no danger of contracting the disease. In the conclusion of the research, it was noted that most patients diagnosed with Alzheimer's are older than 65 years, indicating that this stage of life tends to trigger brain changes associated with the disease. This finding underscores the importance of considering age as a key factor in the early identification of the disease.
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