Case-based Reasoning System Research Articles

Personalised decision support in the management of patients with musculoskeletal pain in primary physiotherapy care: a cluster randomised controlled trial (the SupportPrim project).

We developed the SupportPrim PT clinical decision support system (CDSS) using the artificial intelligence method case-based reasoning to support personalised musculoskeletal pain management. The aim of this study was to evaluate the effectiveness of the CDSS for patients in physiotherapy practice. A cluster randomised controlled trial was conducted in primary care in Norway. We randomised 44 physiotherapists to (1) use the CDSS alongside usual care or (2) usual care alone. The CDSS provided personalised treatment recommendations based on a case base of 105 patients with positive outcomes. During the trial, the case-based reasoning system did not have an active learning capability; therefore, the case base size remained the same throughout the study. We included 724 patients presenting with neck, shoulder, back, hip, knee, or complex pain (CDSS; n = 358, usual care; n = 366). Primary outcomes were assessed with multilevel logistic regression using self-reported Global Perceived Effect (GPE) and Patient-Specific Functional Scale (PSFS). At 12 weeks, 165/298 (55.4%) patients in the intervention group and 176/321 (54.8%) in the control group reported improvement in GPE (odds ratio, 1.18; confidence interval, 0.50-2.78). For PSFS, 173/290 (59.7%) patients in the intervention group and 218/310 (70.3%) in the control group reported clinically important improvement in function (odds ratio, 0.41; confidence interval, 0.20-0.85). No significant between-group differences were found for GPE. For PSFS, there was a significant difference favouring the control group, but this was less than the prespecified difference of 15%. We identified several study limitations and recommend further investigation into artificial intelligence applications for managing musculoskeletal pain.

Penerapan Metode Cased Based Reasoning dalam Mendiagnosa Penyakit TB Paru Berbasis Web

Collaboration of computer science disciplines with other disciplines has been widely carried out, for example, medical science. Expert systems for disease diagnosis are one of the many computer programs used by doctors to assist them in their work and provide good results. Researchers in the field of artificial intelligence are working to improve existing systems to cover their shortcomings. Expert systems are used in many applications for disease diagnosis. Expert systems have been used in various industries and have had a significant impact. An innovative method for diagnosing diseases is the application of case-based reasoning systems, compared to expert systems. The solution to diagnosing and treating diseases can be found in the expert system for diagnosing pulmonary tuberculosis. Because this web-based system is based on a web application, all pulmonary tuberculosis patients can access it. A rule-based system that applies the CBR method can identify various types of diseases through the use of weighting techniques and offer treatment recommendations.

Impact of Artificial Intelligence on Marketing – A Conceptual Study

The marketing field has undergone a considerable transformation with the introduction of AI, leading to enhanced performance. The present study is aimed at finding the effect of AI on marketing. This paper includes an in-depth literature review that offers a complete understanding of the application of AI in marketing. Various studies emphasize significant AI applications in marketing, such as neural networks, case-based reasoning, and expert systems, marking a transformative shift from traditional marketing methods. The integration of AI in marketing leverages technologies like natural language processing, machine learning, and sentiment analysis to enhance decision-making processes, providing precise insights into customer lifecycles and market trends. By combining AI with customer and brand experience data, businesses gain a competitive edge and are better equipped to navigate the dynamic marketplace. The paper underscores the key elements of incorporating AI in marketing functions to enhance overall business performance, leading to increased profitability and a competitive advantage. Its objective is to document insights into the AI ecosystem, elucidating how embedded technologies support marketing processes and contribute to organizational success. Furthermore, the paper delves into the synergy between AI and marketing, highlighting their combined potential as the future driving force for successful business organizations.

Case-Based Reasoning System, a tool to calculate more accurate costs in Jalisco construction companies

Ejecutar un proyecto es algo habitual para cualquier tipo de empresa dedicada a la construcción. Presupuesto y tiempo son los principales elementos que se establecen en un proyecto de construcción; todo sistema que permita una estimación más acertada de cualquiera de estos se vuelve una ventaja competitiva decisiva, que ayuda a la empresa a tener una mejor administración de obra en las primeras etapas al reflejarse a lo largo del proyecto. En este documento el objetivo es utilizar el sistema Case-Base Reasoning (CBR), como herramienta para estandarizar precios, computarizar procesos en cálculo de estimaciones de obra y obtener costos más exactos. La cantidad de variables en la metodología forman una herramienta más práctica para evaluar los costos que se dirigen a cada aspecto de la construcción. La automatización de los procesos relacionados con la gestión de costos de obra garantiza resultados más precisos, con base en la cuantificación de los elementos en el proyecto para elaborar presupuestos de construcción, y obtener los datos que utilizan en este estudio: por medio del sistema CBR.

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System

Toward Automated Knowledge Discovery in Case-Based Reasoning

Automated Case Elicitation (ACE) enables case-based reasoning (CBR) systems to automatically acquire knowledge through real-time exploration and interaction with environments. CBR is an explainable AI methodology, where decisions are based on previous encounters. ACE combined with CBR continues learning as it is being deployed, and produces specific cases that can be reviewed by humans, unlike pretrained large language models (LLMs) that learn by training offline on prior data. ACE and CBR may be useful methods to gather training data for use with generative AI, or to help them to adapt on the fly. This research explores ACE's potential by applying it to chess and conducting extensive experiments against Stockfish, the world's highest rated chess engine. An ACE agent was developed that combines random exploration with shallow alpha-beta search for novel game states. Results over 1000+ games showed the ACE player defeated Stockfish in nearly 10% of games—a notable achievement given Stockfish's extreme strength. Notably, the ACE agent required only 0.1 seconds per game compared an average of 8 minutes for Stockfish, while still gradually improving its win rate through accrued experience. Detailed analyses revealed how the relaxation of ACE's case matching criteria along with selective retention of useful cases enabled accumulation of strategic chess knowledge. The research provides valuable insights into ACE's proficiency for knowledge discovery in complex, adversarial domains. It lays groundwork for integrating ACE, an unsupervised CBR learner, with modern deep learning techniques like neural networks and large language models to combine the strengths of symbolic and subsymbolic AI. By demonstrating ACE's ability to extract strategic knowledge against world-class opponents, this work highlights its potential for impact across gaming, autonomous systems, and other complex problem-solving domains.

Decision Support for Managing Common Musculoskeletal Pain Disorders: Development of a Case-Based Reasoning Application.

Common interventions for musculoskeletal pain disorders either lack evidence to support their use or have small to modest or short-term effects. Given the heterogeneity of patients with musculoskeletal pain disorders, treatment guidelines and systematic reviews have limited transferability to clinical practice. A problem-solving method in artificial intelligence, case-based reasoning (CBR), where new problems are solved based on experiences from past similar problems, might offer guidance in such situations. This study aims to use CBR to build a decision support system for patients with musculoskeletal pain disorders seeking physiotherapy care. This study describes the development of the CBR system SupportPrim PT and demonstrates its ability to identify similar patients. Data from physiotherapy patients in primary care in Norway were collected to build a case base for SupportPrim PT. We used the local-global principle in CBR to identify similar patients. The global similarity measures are attributes used to identify similar patients and consisted of prognostic attributes. They were weighted in terms of prognostic importance and choice of treatment, where the weighting represents the relevance of the different attributes. For the local similarity measures, the degree of similarity within each attribute was based on minimal clinically important differences and expert knowledge. The SupportPrim PT's ability to identify similar patients was assessed by comparing the similarity scores of all patients in the case base with the scores on an established screening tool (the short form Örebro Musculoskeletal Pain Screening Questionnaire [ÖMSPQ]) and an outcome measure (the Musculoskeletal Health Questionnaire [MSK-HQ]) used in musculoskeletal pain. We also assessed the same in a more extensive case base. The original case base contained 105 patients with musculoskeletal pain (mean age 46, SD 15 years; 77/105, 73.3% women). The SupportPrim PT consisted of 29 weighted attributes with local similarities. When comparing the similarity scores for all patients in the case base, one at a time, with the ÖMSPQ and MSK-HQ, the most similar patients had a mean absolute difference from the query patient of 9.3 (95% CI 8.0-10.6) points on the ÖMSPQ and a mean absolute difference of 5.6 (95% CI 4.6-6.6) points on the MSK-HQ. For both ÖMSPQ and MSK-HQ, the absolute score difference increased as the rank of most similar patients decreased. Patients retrieved from a more extensive case base (N=486) had a higher mean similarity score and were slightly more similar to the query patients in ÖMSPQ and MSK-HQ compared with the original smaller case base. This study describes the development of a CBR system, SupportPrim PT, for musculoskeletal pain in primary care. The SupportPrim PT identified similar patients according to an established screening tool and an outcome measure for patients with musculoskeletal pain.

A fuzzy ontology-based case-based reasoning system for stomach dystemperament in Persian medicine.

In Persian medicine, early diagnosis and treatment of stomach dystemperament is crucial for preventing other diseases. However, traditional medicine diagnosis often involves ambiguous and less structured information making it challenging for practitioners. Integrating fuzzy ontology with case-based reasoning (CBR) systems can enhance diagnostic accuracy in this filed. This study aimed to develop and evaluate a fuzzy ontology-based CBR system for diagnosing and treating stomach dystemperament in Persian medicine. This was a mixed-methods research in which a fuzzy ontology-based CBR system was developed based on the fuzzy features, utilizing trapezoidal, triangular, right shoulder and left shoulder membership functions to represent linguistic variables such as hunger level and digestion power. The research phases included identifying relevant terms, concepts, and relationships, developing the fuzzy case-base ontology using the IKARUS-Onto methodology, and subsequently designing and implementing the CBR system. The system performance was evaluated in terms of its sensitivity, specificity, accuracy, precision, and F1-score. Initially, a case-base fuzzy ontology was created. Then, the database was built up using 88 expert-validated medical records. Of these cases, 72% (63 cases) were diagnosed with phlegmatic dystemperament, 18% (16 cases) with cold-dry dystemperament, and 10% (9 cases) had no stomach dystemperament. The CBR system was developed and evaluated using sensitivity, specificity, accuracy, precision, and F1-score which were 97.5%, 87.5%, 96.6%, 98.7%, and 98.1%, respectively. Our fuzzy ontology-based CBR demonstrated high performance in diagnosing stomach dystemperament in Persian medicine. This system shows promise in improving diagnostic accuracy and facilitating the identification of similar cases. While initial results are encouraging, further evaluation in a real clinical environment is recommended to fully assess its practical utility.

A comprehensive review on state-of-the-art applications of case based reasoning in aluminum foundries

An Aluminium Foundry offers multiple opportunities for implementing expert systems across various domains, including component design, process planning, rejection reduction, and determining parameter values for Die-to-Die Design to achieve cost benefits. Considering the projected growth of the Global Aluminium Die Casting market at a 15 percent CAGR until 2025, one of the key hurdles to this growth lies in the requirement for substantial investments. This paper explores the diverse applications of expert systems in different aspects of aluminium casting, focusing on the utilization of Case-Based Reasoning (CBR) systems within Aluminium foundries. Case-Based Reasoning, a problem-solving approach, leverages past cases resembling the current problem to guide solutions for new challenges. The review encompasses various expert systems applied in aluminium casting and specifically highlights the application of Case-Based Reasoning systems. Additionally, it identifies gaps within the Die Design Support System for Gravity Die Casting utilizing Case-Based Reasoning, aiming to delve deeper into this particular area for potential improvements.

Building an enhanced case-based reasoning and rule-based systems for medical diagnosis

Expert systems are computer programs that use knowledge and reasoning to solve problems typically solved by human experts. Expert systems have been used in medicine to diagnose diseases, recommend treatments, and plan surgeries. Interpretability of the results in medical applications is crucial since the decision that will be taken based on the system’s output has a direct effect on people’s health and lives which makes expert systems ideal choices when dealing with these applications in contrast to other machine learning approaches. An expert system has the ability to explain its own line of reasoning providing a robust way of diagnosis. This paper presents two types of expert systems for medical diagnosis. The first system is a case-based reasoning system using a database of previously diagnosed cases to diagnose a new case. The second system is a rule-based expert system that uses a set of if–then rules extracted from a decision tree classifier to make diagnoses. In this paper, machine learning-based similarity functions are proposed and compared with other traditional similarity functions. The results of this study suggest that expert systems can be a valuable tool for medical diagnosis. The two systems presented in this paper achieved competitive results, and they provide diagnoses similar to those made by human experts.

Case-based Maintenance Model for handling Relevant and Irrelevant Cases in Case-based Reasoning System

Case-based Maintenance Model for handling Relevant and Irrelevant Cases in Case-based Reasoning System

An Artificial Intelligence based automated case-based reasoning (CBR) system for severity investigation and root-cause analysis of road accidents – Comparative analysis with the predictions of ChatGPT

Road accidents have been progressively causing havoc in our society and certain preventive measures must be taken to reduce or possibly eliminate road accidents. The derivative of a road accident ranges from a mild injury to casualty. This research work mainly focusses on developing a novel case-based reasoning system to investigate and troubleshoot the cause of road accidents on a war-foot basis. First, the dominant attributes contributing to the cause of road accidents are identified and finalized as 28. A unique road accident dataset is developed which comprises of 1028 data collected from web resources, popular news magazines and extended further to large scale database of one-million cases by biased random number simulation. Each attribute is given a severity weightage of 1,2 and 3 for computing the net weighted score for a case in the database. Also, non-weighted scores are computed by introduction of a primary number dataset to maintain the uniqueness of the score which is further used for similarity analytics. Now, an accident news is randomly selected, and Rapid automatic keyword extraction (RAKE) schema is used as Natural language processor (NLP) for extracting the dominant keywords from the news articles. The extracted keywords are compared and further mapped into a factor-matrix comprising 28 attributes causing road accidents. Further, similarity analytics is performed to evaluate the severity scores and comparison of new cases. The system demonstrated high retrieval accuracy with all road accident cases collected from real world scenarios. This research has great prospects on troubleshooting road accident cases effectively and provides instant promising troubleshooting measures to prevent such accidents in the future. Also, the proposed framework might be useful for intelligent decision-making systems and automated driving systems. Based on the final outlook, a comprehensive framework for national road safety could be developed and passed as a valid law for implementation. Finally, the forecasted results of the proposed algorithm are compared with the predictions of Chat GPT program.

A case-based reasoning approach for solving schedule delay problems in prefabricated construction projects

The project schedule delay problem has received considerable attention for its significance in impeding project performance. Compared to conventional projects, prefabricated construction projects could suffer more from schedule delay problems. A possible solution to such problems is to provide countermeasures by referring to valuable experience of historical cases. This paper proposes a case-based reasoning (CBR) model to leverage past experience and knowledge to address the schedule delay problems in prefabricated construction projects. The study explains the development processes of the CBR model in detail, including the case base construction, case retrieval, case reuse and update. Subsequently, the study develops the CBR system and presents a case study to demonstrate the validity and practicality of the proposed approach. This study provides a potential tool to facilitate schedule management of prefabricated construction projects. It offers an effective reference for decision-makers, such as project managers, to tackle schedule delays through experience mining.

A novel breast cancer detection architecture based on a CNN-CBR system for mammogram classification

This paper presents a novel framework for breast cancer detection using mammogram images. The proposed solution aims to output an explainable classification from a mammogram image. The classification approach uses a Case-Based Reasoning system (CBR). CBR accuracy strongly depends on the quality of the extracted features. To achieve relevant classification, we propose a pipeline that includes image enhancement and data augmentation to improve the quality of extracted features and provide a final diagnosis. An efficient segmentation method based on a U-Net architecture is used to extract Regions of interest (RoI) from mammograms. The purpose is to combine deep learning (DL) with CBR to improve classification accuracy. DL provides accurate mammogram segmentation, while CBR gives an explainable and accurate classification. The proposed approach was tested on the CBIS-DDSM dataset and achieved high performance with an accuracy (Acc) of 86.71 % and a recall of 91.34 %, outperforming some well-known machine learning (ML) and DL approaches.

A case-based reasoning system for neonatal survival and LOS prediction in neonatal intensive care units: a development and validation study

Early prediction of neonates' survival and Length of Stay (LOS) in Neonatal Intensive Care Units (NICU) is effective in decision-making. We developed an intelligent system to predict neonatal survival and LOS using the "Case-Based Reasoning” (CBR) method. We developed a web-based CBR system based on K-Nearest Neighborhood (KNN) on 1682 neonates and 17 variables for mortality and 13 variables for LOS and evaluated the system with 336 retrospectively collected data. We implemented the system in a NICU to externally validate the system and evaluate the system prediction acceptability and usability. Our internal validation on the balanced case base showed high accuracy (97.02%), and F-score (0.984) for survival prediction. The root Mean Square Error (RMSE) for LOS was 4.78 days. External validation on the balanced case base indicated high accuracy (98.91%), and F-score (0.993) to predict survival. RMSE for LOS was 3.27 days. Usability evaluation showed that more than half of the issues identified were related to appearance and rated as a low priority to be fixed. Acceptability assessment showed a high acceptance and confidence in responses. The usability score (80.71) indicated high system usability for neonatologists. This system is available at http://neonatalcdss.ir/. Positive results of our system in terms of performance, acceptability, and usability indicated this system can be used to improve neonatal care.

A case-based reasoning tool to recommend drinking water source protection actions

In response to a need expressed by water actors to support them in identifying appropriate actions for protecting drinking water sources, we modelled, designed, implemented, tested, and validated a prototype case-based reasoning (CBR) system. This required an intensive knowledge acquisition and structuring process which we conducted in collaboration with 102 water management and governance actors in Quebec, Canada. Knowledge was organized in a case base containing nearly 200 past experiences implementing water protection actions at different decision-making scales (local, regional, provincial), by various organizations (government, municipalities, water-related associations), over the last two decades. This paper describes our case engineering process to design case attributes based on content analysis. It presents the case edition, the case retrieval process, and the prototype's implementation and validation, conducted through a rigorous and transparent participatory procedure. This is, to our knowledge, the first successful attempt to apply a CBR approach to support water actors in protecting drinking water sources. It provides empirical evidence of the positive potential of such an approach for knowledge sharing and transfer and can be generalized to other similar contexts.

CASE BASED REASONING (CBR) FOR OBESITY LEVEL ESTIMATION USING K-MEANS INDEXING METHOD

As many as 600 million of the 1.9 billion adults who are overweight are obese. Obesity that is not treated immediately will be a risk factor for increasing cardiovascular, metabolic, degenerative diseases, and even death at a young age. Case Based Reasoning (CBR) can be used to estimate a person's obesity level using previous cases. The old case with the highest similarity will be the solution for the new case. Indexing methods such as the K-Means Algorithm are needed so that the search for similar cases does not involve all cases on a case base so that it can shorten the computation time at the retrieve stage and still produce optimal solutions. Cosine similarity is used to find relevant clusters of new cases and Euclidean distance similarity is used to calculate similarity between cases. Random subsampling method was used to validate the CBR system. The test results with K=2 indicate that the CBR is better than the CBR-K-Means, each of which produces an average accuracy of 88.365% and 88.270% at a threshold of 0.8. CBR-K-Means produces an average computation time at the retrieve stage of 33.55 seconds and is faster than the CBR of 35.5 seconds.

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.

The Integration of Hadoop and a Smart Utility Scoring with a Case-Based Reasoning System for Managing Large and Complex Medical Case Base

In case-based reasoning structure, the quality and the complexity of the case data play a significant role in searching, retrieving, updating, and holding distinct case data at or from distinct databases for various purposes. However, managing the very complex and large case-data sets is not easy. Furthermore, the complex algorithm for maintaining case-based reasoning (CBR) structure makes it more critical and increases the time complexity considerably when the dataset is large and complex. Consequently, distinct available techniques related to the maintenance of the CBR system concentrate on deleting the less important and very complex case data to reduce the number of cases. At the same time, it reduces the efficiency and effectiveness of handling the CBR system. Hence, this research employs an integrated platform with the combination of the Hadoop parallel platform and an intelligent utility scoring mechanism. The smart indexing system and the parallel processing capacity of Hadoop reduce the time complexity for processing large and complex datasets. The distributed storage capability effectively manages the data repository and retrieval system for enormous case datasets without compromising case data. The intelligent utility scoring system employs supervised learning and a unique CBR system to quickly and efficiently retrieve the effective solution for each particular case. Furthermore, Hadoop offers a distributed structure that helps the user to access the case data and update the database through the network. This research uses distinct healthcare text-based datasets for testing the performances of the proposed integrated technique in different aspects. The experimental results show the superiorities in producing a higher percentage of accuracy with consuming less Retrieval time over other related techniques. It further shows its time efficiency by offering higher throughput and lower Cyclomatic Complexity with lower read and write Latency Times. Finally, the performances of the proposed technique in these various aspects have been compared with distinct existing techniques and the performances of various databases in managing large and complex medical data to establish its superiority over them.

APPLICATION OF CASE BASED REASONING (CBR) METHOD IN DISTRIBUTION TRAFO DAMAGE EXPERT SYSTEM AT PLN UP3 BINJAI

In the world of electricity, transformers are widely used, both in the field of electric power and electronics. The use of transformers in the electric power system is used for each of its needs, for example the need for high voltage in the delivery of electrical power over long distances. Electrical equipment transformers are expensive and are very vital equipment. If the transformer is disturbed or in an abnormal condition, there may be an unexpected temporary cessation of electricity distribution and will cause losses for PLN and consumers who use electricity by disrupting the activities carried out. The Case Based Reasoning (CBR) method is a case-based reasoning system that uses previous experiences or cases so that they can solve new problems or cases. There are several stages in the case based reasoning method, including retrieve, reuse, revise, and retain. Case-based Reasoning (CBR) collects previous cases that are similar to the new problem and tries to modify the solution to fit the new case. The basic idea of ​​Case-Based reasoning is the assumption that similar problems have similar solutions. While this assumption is not always true, it does depend on many practical domains. Where the previous case experienced an overvoltage transformer damage, using the CBR method will look for similarities to the previous case with existing characteristics, the CBR method will look for the same case on the existing Knowladge so that the damage is quickly detected. From the characteristics of the damage that is on the transformer, it is easy to diagnose transformer damage using the CBR method and the results of the CBR method will find a ranking that is almost similar to the existing case.