Application of Machine Learning on MRI Scans for Alzheimer's Disease Early Detection

Early detection of diseases is crucial for effective prevention and treatment. Machine learning techniques offer promising avenues to streamline medical examinations, potentially reducing costs and improving outcomes. In this experiment, a simpler step was taken, by directly converting pre-processed MRI scans into two-dimensional arrays. Then, Principal Component Analysis (PCA) and Synthetic Minority Over-sampling Technique (SMOTE) was employed to address data imbalance and reduce the data's dimensionality. This pre-processed dataset underwent rigorous testing using a 10-fold cross-validation approach. The standout result of this experiment was the implementation of the Extra Trees algorithm, which achieved an impressive accuracy score of 99.0%. The model also displayed remarkable performance in terms of Precision, Recall, and F1 Score, all reaching a confidence level of 98.9%. These results underscore the potential of machine learning not only in accurate disease early detection, but also in managing data intricacies such as imbalance and high dimensionality. This achievement can serve as a foundation for future research, encouraging scientists to delve deeper into other potential biomarkers and imaging techniques for early Alzheimer's Disease detection. The experiment's success suggests that by building upon this methodology and harnessing the power of advanced machine learning algorithms, we can unlock new possibilities in the realm of medical diagnostics.