Comparison of machine learning algorithms for alzheimer's risk classification
Keywords:
Alzheimer's disease, Machine learning algorithms, XGBoost, Disease classification, Health data analysisAbstract
Alzheimer's disease is a progressive form of dementia that affects cognitive function and impacts the quality of life of sufferers. Advancements in artificial intelligence, particularly in machine learning, are creating new possibilities for streamlining the disease classification process using health history data. This study aims to compare the performance of five machine learning algorithms, namely Naive Bayes, Random Forest, Artificial Neural Network (ANN), XGBoost and Support Vector Machine (SVM), in predicting Alzheimer's diagnosis using 2,149 data from Kaggle Open Datasets. The data went through the process of Data Collection, preprocessing, testing using the five algorithms. The evaluation results show that XGBoost has the highest accuracy of 95%, followed by Random Forest with 93% accuracy, ANN 84%, SVM 83%, and Naive Bayes 83%. In conclusion, XGBoost proved to be the most effective model in detecting Alzheimer's, ahead of the other algorithms in this study.
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