Table 1.

An overview of AI models with brief descriptions.

Algorithm	Overview	Advantages	Disavantages
Support vector machine	A supervised ML model used for both classification and regression. It works by finding an hyperplane that maximally separates data points of different classes in a high-dimensional space, aiming to maximize the margin between the classes.	Effectively handles unstructured and semi-structured data Low generalization error	Requires long training time for large datasets Results may be difficult to interpret
Logistic regression	A supervised ML algorithm used for binary classification. It models the probability of an instance belonging to a particular class using the logistic function, and the decision boundary is a linear combination of input features.	Provides insight into feature relevance Efficient for small datasets Rapid training	Assumption of linearity Sensitivity to outliers
Random forest	A supervised ML algorithm used for classification and regression tasks. It builds multiple decision trees during training and merges their predictions to improve accuracy and robustness. Each tree is trained on a random subset of the data, and the final prediction is determined by a majority vote (for classification) or an average (for regression).	High accuracy in capturing complex relationships in data Efficient on large datasets Provides insight about feature relevance	Sensitive to small changes in data Bias towards dominant classes
Gradient-boosted decision tree	An ensemble learning technique used for both classification and regression. It builds a series of decision trees sequentially, with each tree correcting the errors of the previous ones. It combines the predictions of individual trees to create a strong predictive model.	High accuracy in capturing complex relationships in data Provides insight into feature relevance Effective on structured and unstructured data	Requires long training time for large datasets Complex interpretability
k-nearest neighbour	A supervised ML algorithm for classification and regression that predicts a data point’s label or value based on the majority of its nearby neighbors in the dataset. The “k” represents the number of neighbors considered for the prediction.	No training period Supports dynamic data addition Efficient for small datasets	Poor performance with large datasets Impact of irrelevant features Sensitivity to outliers and missing data
Naive Bayes	A supervised ML algorithm for classification. It is based on Bayes’ theorem and assumes independence between features. The algorithm calculates the probability of a data point belonging to a particular class by considering the probabilities of its individual features.	Simple and fast Requires small amount of training data Less sensitive to irrelevant features	Assumption of feature independence “Zero Probability” issue Sensitivity to outliers and missing data
Convolutional neural network	A DL algorithm designed for image and video recognition. It uses convolutional layers to automatically and adaptively learn spatial hierarchies of features from input data.	Learns hierarchical features from spatial data Allows parameter sharing, reducing overfitting Automated feature learning	Large datasets needed Requires long training time Complex interpretability