Cognitive Decision Support System for medical diagnosis

Chandiok, Ashish; Chaturvedi, D. K.

doi:10.1109/icctict.2016.7514604

Cited by 4 publications

(3 citation statements)

References 13 publications

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“… It is a neuro-fuzzy algorithm that can help to solve decision making problems such as the COVID-19 classification problem. The strength of FCM in cognitive decision making during medical diagnosis was presented in ( Chandiok and Chaturvedi, 2016 ). It was noted that FCM helps to represent the cognitive knowledge required in expert systems that are used for medical diagnosis.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease

Akinnuwesi

Fashoto

Mbunge

et al. 2021

Data Science and Management

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Clinical methods are used for diagnosing COVID-19 infected patients, but reports posit that, several people who were initially tested positive of COVID-19, and who had some underlying diseases, turned out having negative results, after further tests. Therefore, the performance of clinical methods is not always guaranteed. Moreover, chest X-ray image data of COVID-19 infected patients are mostly used in the computational models for COVI-19 diagnosis while the use of common symptoms such as “ Fever, Cough, Fatigue, Muscle aches, Headache etc ”, in computational models is not yet reported. In this study, we employ seven classification algorithms to empirically test and verify their efficacy when applied to diagnose COVID-19 using the aforementioned symptoms. We experimented with logistic regression (LR), support vector machine (SVM), naïve Byes (NB), decision tree (DT), multilayer perceptron (MLP), fuzzy cognitive map (FCM) and Deep neural network (DNN) algorithms. The techniques were subjected to random under-sampling and over-sampling. Our results showed that with class imbalance, MLP and DNN outperform others but without class imbalance MLP, FCM and DNN outperform others with the use of random under-sampling but DNN has the best performance with the use random oversampling. This study identified MLP, FCM and DNN as better classifiers over LR, NB, DT and SVM, that healthcare software system developers can adopt to develop intelligence-based expert systems which both medical personnel and patients can use for differential diagnosis of COVID-19 based on the aforementioned symptoms however, the test of performance must not be limited to the traditional performance metrics.

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Section: Literature Reviewmentioning

confidence: 99%

Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease

Akinnuwesi

Fashoto

Mbunge

et al. 2021

Data Science and Management

View full text Add to dashboard Cite

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“…Croskerry (2003) details 32 different types of cognitive biases that may lead to diagnosis errors in the medical field (Croskerry 2003). The process of making a medical diagnosis involves considering numerous factors systematically to make a decision (Wagner 1993; de Haes & Bensing 2009; Chandiok & Chaturvedi 2016). Similarly, cognitive activities used in the engineering design process involve systematical considering factors to develop a solution via brainstorming or reflective processes (Seidel & Fixson 2013).…”

Section: Introductionmentioning

confidence: 99%

Uncovering potential bias in engineering design: a comparative review of bias research in medicine

Agyemang,

Andreae,

McComb

2023

Des. Sci.

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Engineering design research has focused on developing and refining methods and evaluating design education in design education, design research and design in practice. One important aspect that is not thoroughly investigated is the influence of bias on design within these spaces of design. Bias is known to impact the interpretation of information, decision-making and practices in all areas. These factors are vital in engineering design education, practice and research, emphasizing the importance of investigating bias. The first goal of this study is to highlight and synthesize existing bias research in design education, research and practice. The second goal is to identify areas where bias may be under-researched or under-reported in design. To achieve these goals, a comparative analysis is performed against a comparable field: medicine. Many parallels exist between both fields. Patient–provider and designer–end-user relationships are comparable. Medical education is comparable to design education with the cooperative, inquiry-based and integrated learning pedagogy approaches. Lastly, physicians and design engineers both solve cognitively complex systems-oriented problems. Leveraging research on bias in medicine enables us to highlight gaps in engineering design. Recommendations are made to help design researchers address these gaps.

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“…These models also lack the human-like multi-criteria decision making. So, for developing a better medical diagnostic expert system and remove the problem of static and abstract knowledge development with human-like cognitive inference, this work proposes a novel architecture model for a diagnostic expert system in continuing work of Chandiok et al, [7].…”

Section: Related Workmentioning

confidence: 99%

A Cognitive Inference Approach for Developing Medical Diagnostic Expert Systems

Chandiok¹,

Chaturvedi²

2018

IJCA

Self Cite

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Cognitive approaches are nowadays the most popular and widely used means of computing information due to its human-like sensing, comprehending, and action. The cognitive system can handle diverse data, uses modern technologies like natural language processing, machine learning, semantics, and decision support. It can have human interaction behavior with contextual data handling. The decisions are also not made on fixed rules, but human-like weight based judgments. Expert systems (ES's) are intelligent software tools that use acquired knowledge from experts in a specific domain to offer assistance to its users over a dialog or a query talks conducted between the user and the ES application software. This research work is for implementing a cognitive approach for determining diagnosis decisions providing expert system services. Cognitive strategies frequently denoted as "Human-like intelligent Computing Method" using restricted processing, storing and displaying skills. This paper presents a new diagnosis problem resolving model grounded in the investigation of the relationships among symptoms and illnesses in the form of certainty and severity elements. The article also acquaints with a modified knowledge representation appropriate for a cognitive system with limited structured data handling capabilities using XML language.The proposed model thrived and tested in the domain of medicals like fever (Flu, Pneumonia, and Cold Fever) using real knowledge base acquired by the mayo clinic. The earned consequences establish the strength and competence of the planned model. General Terms

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Cognitive Decision Support System for medical diagnosis

Cited by 4 publications

References 13 publications

Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease

Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease

Uncovering potential bias in engineering design: a comparative review of bias research in medicine

A Cognitive Inference Approach for Developing Medical Diagnostic Expert Systems

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