Data analytics is an integral part of strategic decision making in various fields but not limited to business, education and healthcare systems. Existing research works focus on the discovery of itemsets with rare antecedents and consequent or frequent antecedents and consequent. Analysis of association among itemsets with rare antecedents and frequent consequent is equally important to gain valuable insights before making crucial decisions. Mining these itemsets from large datasets is time and resource intensive process. Expedition in the process of mining aids in quick decision making and hence, the entire dataset needs to be stored in the RAM. In this paper, a novel Weighted Binary Count Tree (WBIN-Tree) is proposed and implemented in CUDA to exploit the power of GPU and discover rules with rare antecedent and frequent consequent using parallel approach. WBIN-Tree stores the entire dataset in an abstract, complete and compact form in the RAM using single database scan. WBIN-Tree is compared with existing sequential and parallel algorithms by varying the data size and dimension. The performance evaluation of WBIN-Tree showed promising results, proving to be the most time and space efficient algorithm to store the entire large dataset in the RAM. However, based on the size of the GPU, the performance drops when executed on datasets with large dimensions which could be handled by processing the attributes in batches. Additionally, a case study is included to understand the importance of mining association rules with rare antecedent and frequent consequent by executing the algorithm on breast cancer dataset.

Sarcoidosis, one of the rarest diseases, is challenging to diagnose as it mimics the symptoms of other diseases. Machine learning algorithms identify hidden patterns among symptoms, making them suitable for early diagnosis of Sarcoidosis. In this study, four ensemble models are developed using baseline classifiers and applied to a symptom-based secondary dataset to explore the hidden information. The dataset comprises 189 patient records with 14 attributes: 2 serum markers, 10 symptoms, the patient’s gender, and 1 target variable. An exploratory data analysis is carried out using necessary preprocessing techniques, including missing value imputation and data scaling. The features are selected using PCA, and the relevance of the features is analyzed using the Chi-Square Test, Mutual Information, Sequential Feature Selection, and Tree-Based Selection methods. CTGAN, a GenAI technique, is used to augment the dataset, as it contains only 189 records. CTGAN preserves the clinical fidelity of all the features pertaining to the diagnosis of Sarcoidosis, ensuring synthetic data retains meaningful diagnostic patterns. The performance of the models developed is evaluated by applying them to both the original and synthetic data. Results demonstrate that proposed ensemble methods, Model Combinations 1, 3, and 4, showed 99.47% accuracy on the original dataset, whereas Model Combination 1 and Random Forest classifier showed 85.19% and 60.78% accuracies on a combination of the original with 81 synthetic and 1000 synthetic data, respectively. This highlights the combined advantage of CTGAN-based augmentation and ensemble learning in enhancing diagnostic modeling for rare diseases like Sarcoidosis where the datasets are available with limited data points.

Data analytics has gained popularity due to its ability to extract valuable insights from large amounts of data, allowing businesses to make appropriate decisions and improve overall performance. Expediting this process requires the entire dataset to reside in the main memory. This study proposes a novel Weighted Compact Tree (WC-Tree), a tree data structure that represents the complete dataset in abstract form in the RAM, leading to fast data retrieval. Its significance is analyzed by executing it on publicly available datasets and comparing it against existing algorithms. The analysis indicates that WC-Tree is efficient on datasets of any size with varying attributes. The efficacy of WC-Tree is further analyzed by executing it on acute inflammation data containing patient records on acute cystitis and pyelonephritis, two urological diseases commonly caused by urinary tract infections. Early detection of these diseases using a cost and time-effective method is useful in their diagnosis. WC-Tree aims to discover the association among various attributes in predicting these urological diseases using association rule mining. Association rules are identified based on null-invariant: Cosine, Kulczynski, and null-variant: Conviction, Confidence, and Lift evaluation measures. The rules discovered show that if lumbar pain, pain during micturition, straining to void, inflammation of the urinary bladder, a rare symptom, is detected, then there are chances of the patient experiencing high body temperature and pyelonephritis.