A computer diagnostic system for the diagnosis of prolonged undifferentiating liver disease

Introduction. Endometriosis is a difficult-to-diagnose pathology due to the diversity of clinical manifestations and the lack of high-precision markers necessary for rapid noninvasive diagnosis and timely administration of pathogenetically justified treatment.The aim of this work was to develop a computer system that allows us to assess the probability of endometriosis with various localizations in women, based on artificial neural networks.Material and Methods. The neural network mathematical models were constructed and tested based on data from 110 patients with morphologically pre-confirmed endometriosis. Patients were divided into training and test samples. The models were built based on anamnestic data and results of proteomic and enzyme immunoassays in blood plasma samples.Results and Discussion. In the course of the study, four mathematical models of neural networks were constructed to predict the presence or absence of endometriosis in a woman and its localization if present. Based on these mathematical models, a computer system “Differential diagnosis of endometriosis” was developed. This system allowed to assess the probability and localization of endometriosis in a patient based on parameters obtained as a result of neural network training.Conclusion. The developed computer diagnostic system allowed predicting the presence of endometriosis and its localization with a probability over 80%, depending on the predicted localization, based on data about the patient and the results of her examination. This system may be used for differential diagnosis of endometriosis from other diseases of the female reproductive system, as well as for differential diagnosis of various endometriosis localizations.

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A SURVEY ON CLASSIFICATION OF LIVER TUMOUR FROM ABDOMINAL COMPUTED TOMOGRAPHY USING MACHINE LEARNING TECHNIQUES

EPRA International Journal of Research & Development (IJRD) ◽

10.36713/epra6570 ◽

2021 ◽

pp. 148-156

Author(s):

Gunasundari S ◽

Swetha R

Keyword(s):

Machine Learning ◽

Computed Tomography ◽

Liver Disease ◽

Liver Cancer ◽

Diagnostic System ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Significant Area ◽

System Benefit ◽

Sensitivity Specificity

Pattern recognition is a significant area of research in medicine because many applications like diagnostic system benefit from it. The aim of this research is to analyze developments of liver cancer detection using machine learning techniques for liver disease. The study highlights how liver cancer diagnosis is assisted using machine learning with supervised, unsupervised and deep learning techniques. Several state of art techniques are compared based on performance measures such as accuracy, sensitivity, specificity. Finally, challenges are also highlighted for possible future work. KEYWORDS: Machine Learning, Liver, Liver disease, Computer Aided Diagnosis system, Liver Cancer, Computed Tomography

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