E. C. Igodan

Hepatitis is a major global health issue, impacting millions worldwide. Early detection and precise diagnosis are essential for effective treatment and preventing complications. This study proposes a predictive model for hepatitis diagnosis using ensemble feature selection techniques and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The goal is to develop a robust and accurate model to assist healthcare professionals in promptly diagnosing hepatitis cases. The methodology follows the CRISP-DM framework, including data collection, preprocessing, ensemble feature selection, ANFIS model development, and evaluation using performance metrics like accuracy, precision, recall, and F1-score. Publicly available hepatitis datasets are utilized for experimentation and validation. The results demonstrate that ensemble feature selection effectively identifies informative hepatitis diagnosis features, enhancing the model's predictive performance. The ANFIS model, trained on the selected features, achieves high accuracy and balanced performance metrics, indicating its effectiveness in accurately diagnosing hepatitis cases. The implications include early hepatitis detection, healthcare resource optimization, and potential for personalized medicine. Future directions involve integrating additional data sources, continuous model refinement, and clinical validation studies. This study contributes to advancing predictive modeling for hepatitis diagnosis and highlights the importance of interdisciplinary collaboration between healthcare and data science disciplines in addressing global health challenges.

his project delves into the realm of network security through the development and evaluation of an Intrusion Detection System (IDS) that harnesses the power of feature selection and ensemble models. In today's digitally interconnected world, the protection of networks against malicious activities and cyber threats is of paramount importance. IDSs serve as the first line of defence in identifying and mitigating these threats, making their enhancement a critical area of research.
This study demonstrates the efficacy of combining feature selection methods with ensemble models to fortify IDS capabilities. By conducting an extensive review of existing IDS methodologies, collecting network data, and employing ensemble techniques, this research showcases that this approach surpasses traditional feature selection methods not only in accuracy but also in computational efficiency.
The use of ensemble models has rendered the IDS more resilient, adaptable to diverse attack patterns, and robust against the inherent noise in network data. These findings contribute significantly to the field of cybersecurity, shedding light on the potential of uniting feature selection and ensemble models to optimise IDS performance. The practical implications of this research extend to organisations and institutions seeking to bolster their network security posture. Elevating IDS accuracy and efficiency is a pivotal step towards safeguarding networks against the continually evolving landscape of cyber threats.

Breast cancer stays one of the maximum standard and life-threatening illnesses affecting girls globally. Accurate and early diagnosis is critical for effective treatment and improved survival rates. This project explores the application of an Artificial Neuro-Fuzzy Inference System (ANFIS) for the classification of breast cancer. ANFIS combines the learning capabilities of neural networks with the reasoning capabilities of fuzzy logic, creating a hybrid model that can handle the complexities and uncertainties inherent in medical data. The research involves the collection and preprocessing of breast cancer datasets, followed by the design and implementation of an ANFIS model. The model is trained using a portion of the dataset and tested on the remaining data to evaluate its classification performance. Key performance metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve are used to assess the effectiveness of the ANFIS model. Preliminary results indicate that the ANFIS model demonstrates promising accuracy in distinguishing between benign and malignant breast tumors. The adaptive learning process of the ANFIS allows for continuous improvement and adjustment of the model, enhancing its diagnostic capabilities over time. This study highlights the potential of ANFIS as a reliable and efficient tool for breast cancer classification, contributing to the advancement of artificial intelligence applications in medical diagnostic