An Improved Attribute Subset Selector for Alzheimer’s Disease Prediction

To overcome this problem, approaches such as data mining can be used to effectively predict AD.Data mining [4,5] is the process of determining patterns and knowledge and patterns from huge volumes of data.It involves the utilization of statistical models and algorithms to identify knowledge and patterns.Here are some commonly used data mining techniques that can be applied in AD disease prediction: Data pre-processing: Before applying data mining techniques, it is important to pre-process the data [6].This involves cleaning the data, handling missing values, and transforming the data into a suitable format for analysis.Feature selection: Feature selection [7] is the process of identifying the most relevant features or variables that contribute significantly to the AD prediction.Techniques such as correlation analysis, information gain, and principal component analysis can be used to select the most informative features.Classification algorithms: Once the relevant features are identified, various classification algorithms [8] can be applied to build predictive models.Some commonly used algorithms include decision trees, random forests, Support Vector Machines (SVM) [9], logistic regression, and artificial neural networks [10].These algorithms learn from the available data and generate a model that can predict the presence or absence of AD based on the selected features.Cross-validation and model evaluation: It is essential to evaluate the performance of the predictive models to ensure their reliability.Cross-validation techniques, such as k-fold cross-validation, can be used to assess the model's performance on different subsets of the data.Metrics like accuracy, sensitivity, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) can be used to evaluate the model's performance.Ensemble methods: Ensemble methods [11] combine multiple predictive models to improve prediction accuracy.Techniques such as bagging, boosting, and stacking can be employed to create an ensemble of models that collectively make predictions.Validation with independent datasets: Once the predictive model is built, it is important to validate its performance on independent datasets.This helps to ensure that the model's predictive ability is not limited to the dataset on which it was trained.Interpretation and feature importance: Data mining techniques can also provide insights into the most important features contributing to the AD prediction.This can help researchers and clinicians better understand the underlying factors and mechanisms associated with the disease.Feature selection is one of the processes of data mining which reduces the dimensionality of data for reducing the