# The Application of Intelligent Data Models for Dementia Classification
## 3 Results
### Classification Methods and Implementation  
Several machine learning techniques were explored in this study based on 10-fold cross-validation ([PERSON] & [PERSON], 1968) [23]. Naive Bayes (NB) is probabilistic algorithm that is often used in classification tasks ([PERSON], [PERSON], [PERSON] & [PERSON], 2007) [24]. It uses [PERSON]'s theorem to assign a class label to an instance given the probabilities of the other features in the sample ([PERSON], 2004) [25]. The NB classifier has been shown to be successful in practice, often utilized in classification alongside other more complex and sophisticated techniques ([PERSON], 2001) [26]. Repeated Incremental Pruning to Produce Error Reduction (RIPPER) is a rule-based classifier, which was proposed as an optimized version of IREP ([PERSON], 1995) [27]. Classes are examined in increasing size and there is an initial set of rules that is generated for the class using incremental reduced error. RIPPER then proceeds by treating the examples in the training data as a class and finding a set of rules that will cover all members of the respective class. Once rules are derived, then RIPPER eliminates rules that have high misclassifications or cover a minimal number of instances in the dataset. SVMs are a set of supervised learning methods used for classification as well as regression and outlier detection. SVMs can perform binary and multi-class classification tasks. On the other hand, K-nearest neighbors (KNN) is a type of instance learning algorithm. This classification classifies test data by considering the k-nearest neighbor's majority vote within the nearest neighbors of a data point ([PERSON], [PERSON] & [PERSON], 1991) [28]. We used k = 5 [29; 30]; when using k = 5, then more neighboring data instances' class labels will be utilized to assign the class label of the test data during the process of cross validation. This indeed reduces any sort of bias for the class assignment process since more neighbors are used, hence ensuring a collective decision-making. Lastly, C4.5 (48 in WEKA) is a Decision Tree algorithm that produces tree-based classifiers ([PERSON], 1993) [31]. This algorithm is created to predict test data by learning decision rules through the features present in the dataset and finding rules that can apply to all points in a subset. It is capable of processing both binary and multi-class classification datasets. For all algorithms, we used the default hyper parameters. Figure 2: Confusion matrix ([PERSON], 2018) [22]. In our experimentation, we measured the performance of five machine learning algorithms to predict the dementia class of subjects from the ADNI TADPOLE challenge dataset (ADNI, 2017b) [18]. The algorithms were used on a subset of this data, which included demographic and CDR-SB variables to predict the class of each patient. The performance metrics produced for each algorithm are shown in Table 2. NB, C4.5, and RIPPER resulted in the highest performance, as seen across all measures, with all results between 88.4-95.5%. C4.5 arguably performed the best, with the highest result across all metrics excluding ROC. The highest classification accuracy was by the C4.5 algorithm, with 89.7%. This result was 15% higher than SVM (linear) accuracy. The NB and RIPPER algorithms showed similarly competitive results within \(+/-2\%\) when compared to C4.5 across all metrics as shown in Table 2.  
Sensitivity measures the proportion of positive results predicted correctly (Equation (1)). Sensitivity is negatively impacted when results that should be positive are predicted as negative, or a False Negative (FN). Our results indicate that C4.5 was able to correctly identify these results with the smallest proportion of FNs (0/ 341 dementia subjects were classified as CN and 43/866 MCI patients were classified as CN), with a sensitivity score of 89.7%. RIPPER and NB showed similar performance in regard to sensitivity, albeit a 0.7% and 1.3% lower sensitivity rate than C4.5, respectively.