Abstract: Colon cancer is a prevalent and life-threatening disease, necessitating accurate and timely diagnosis for effective treatment and improved patient outcomes. This research paper presents the development of a knowledge-based system for diagnosing colon cancer using the CLIPS language. Knowledge-based systems offer the potential to assist healthcare professionals in making informed diagnoses by leveraging expert knowledge and reasoning mechanisms. The methodology involves acquiring and structuring medical knowledge specific to colon cancer, followed by the implementation of a knowledge- based system using (...) CLIPS. The system's design integrates relevant medical databases and employs advanced reasoning mechanisms to facilitate accurate diagnosis. Evaluation results demonstrate the system's effectiveness, with high accuracy and sensitivity rates compared to existing methods. The findings highlight the potential of knowledge-based systems in improving clinical practice and patient care. The research contributes to the growing field of medical informatics by demonstrating the feasibility and effectiveness of CLIPS-based knowledge systems for colon cancer diagnosis. Future research can focus on enhancing system performance and expanding its scope to encompass other types of cancer diagnoses. (shrink)

Migraine is a complex neurological disorder characterized by recurrent moderate to severe headaches, accompanied by additional symptoms such as nausea, sensitivity to light and sound, and visual disturbances. Accurate and timely diagnosis of migraines is crucial for effective management and treatment. However, the diverse range of symptoms and overlapping characteristics with other headache disorders pose challenges in the diagnostic process. In this research, we propose the development of an expert system for migraine diagnosis using artificial intelligence and the CLIPS (C (...) Language Integrated Production System) framework. The expert system utilizes a rule-based inference engine to analyze patient-reported symptoms and provide reliable diagnoses or probability scores indicating the likelihood of migraine. The knowledge base of the expert system is designed based on expert knowledge obtained from medical professionals specializing in migraines. The collected knowledge is translated into a structured format suitable for the CLIPS inference engine, incorporating rules and facts to represent the diagnostic criteria and associated symptoms. The system prompts users to provide relevant information about their symptoms, medical history, and potential triggers. It applies the defined rules and facts to evaluate the likelihood of migraine and generate accurate diagnoses or probability scores. Preliminary evaluation results demonstrate the potential of the expert system as a valuable tool for diagnosing migraines. A dataset of anonymized patient records with confirmed migraine cases was used to test the system. The diagnoses generated by the expert system were compared against the known diagnoses, and a high level of accuracy was observed, with 90% of cases correctly diagnosed as migraines. These results highlight the effectiveness and reliability of the system in assisting medical professionals in the diagnosis of migraines. The proposed expert system offers several advantages for migraine diagnosis. It leverages the collective knowledge and expertise of experienced migraine specialists, providing a standardized and consistent approach to diagnosis. The system can handle large amounts of patient data and effectively analyse complex relationships between symptoms, risk factors, and diagnostic criteria. Furthermore, it offers real-time feedback and recommendations, supporting medical professionals in their clinical decision-making process. Future work involves refining the expert system based on feedback from medical experts, expanding the knowledge base to encompass a wider range of symptoms and risk factors, and conducting further evaluations to enhance its accuracy and applicability in clinical settings. The development of an expert system for migraine diagnosis has the potential to improve the diagnostic process, leading to more effective management and treatment strategies for individuals suffering from migraines. (shrink)

Brain tumors pose significant challenges in modern healthcare, with accurate and timely diagnosis crucial for determining appropriate treatment strategies. Artificial intelligence has made significant advancements in recent years. Rule-based expert systems (if-then rule-based systems) have emerged as a promising approach for clinical decision-making in brain tumor diagnosis. In this paper, we present "A CLIPS-Based Expert System for Brain Tumor Diagnosis," which leverages a set of 14 if-then rules to diagnose brain tumors with three possible outcomes: 1) Confirm the diagnosis of (...) a brain tumor, 2) Consider the possibility of a brain tumor that has metastasized, and 3) Consider the possibility of a brain tumor. Our expert system offers a user-friendly interface, enabling users to select symptoms and receive a diagnosis based on the provided information. This paper discusses the expert system's development, implementation, and evaluation, highlighting its potential to facilitate brain tumor diagnosis and decision-making in clinical settings. Additionally, we provide a literature review that contextualizes our expert system within the broader landscape of rule-based expert systems for brain tumor diagnosis, examining their effectiveness, limitations, and challenges. (shrink)