Taxonomy on Healthcare System Based on Machine Learning Approaches: Tuberculosis Disease Diagnosis

Nowadays computers are widely used in the medical world to aid in the diagnosis of a disease. The most frequently encountered Penyakityang adalahpenyakitTuberculosis. Therefore, prevention of tuberculosis disease begins with diagnosing dini.Salah a technique in diagnosing tuberculosis disease is an expert system. Therefore research inibertujuan construct an expert system that is used for early diagnosis of Tuberculosis disease by gejalayang in anguish. The system displays the amount of credence to the possibility of disease symptoms yangdiderita users. The value of these beliefs using Certainty Factor, because the CF is able to determine the value of trust in a greater uncertainty and be able to demonstrate absolute confidence.

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Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

IEEE Access ◽

10.1109/access.2021.3062484 ◽

2021 ◽

Vol 9 ◽

pp. 37622-37655

Author(s):

Protima Khan ◽

Md. Fazlul Kader ◽

S. M. Riazul Islam ◽

Aisha B. Rahman ◽

Md. Shahriar Kamal ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Disease Diagnosis ◽

Brain Disease ◽

Learning Approaches ◽

Recent Advances

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Review on Data Securing Techniques for Internet of Medical Things

Journal of Soft Computing Paradigm - September 2019 ◽

10.36548/jscp.2021.3.004 ◽

2021 ◽

Vol 3 (3) ◽

pp. 177-191

Author(s):

R. Kanthavel

Keyword(s):

Machine Learning ◽

Health Care ◽

Internet Of Things ◽

Healthcare System ◽

System Performance ◽

Learning Approaches ◽

Wide Range ◽

Security Problem ◽

Internet Of Medical Things ◽

Privacy Issues

In recent days Internet of Things (IOT) has grown up dramatically. It has wide range of applications. One of its applications is Health care system. IOT helps in managing and optimizing of healthcare system. Though it helps in all ways it also brings security problem in account. There is lot of privacy issues aroused due to IOT. In some cases it leads to risk the patient’s life. To overcome this issue we need an architecture named Internet of Medical Things (IOMT). In this paper we have discussed the problems faced by healthcare system and the authentication approaches used by Internet of Medical Things. Machine learning approaches are used to improvise the system performance.

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Machine Learning Approaches in Parkinson’s Disease

Current Medicinal Chemistry ◽

10.2174/0929867328999210111211420 ◽

2021 ◽

Vol 28 ◽

Author(s):

Annamaria Landolfi ◽

Carlo Ricciardi ◽

Leandro Donisi ◽

Giuseppe Cesarelli ◽

Jacopo Troisi ◽

...

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Neurodegenerative Disorder ◽

Learning Algorithms ◽

Disease Diagnosis ◽

Machine Learning Algorithms ◽

Clinical Aspects ◽

Learning Approaches

Background:: Parkinson’s disease is the second most frequent neurodegenerative disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present, no biomarker is available to obtain a diagnosis of certainty in vivo. Objective:: The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson’s disease diagnosis and characterization. Methods:: A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query: “Machine Learning” “AND” “Parkinson Disease”. Results:: the obtained publications were divided into 6 categories, based on different application fields: “Gait Analysis - Motor Evaluation”, “Upper Limb Motor and Tremor Evaluation”, “Handwriting and typing evaluation”, “Speech and Phonation evaluation”, “Neuroimaging and Nuclear Medicine evaluation”, “Metabolomics application”, after excluding the papers of general topic. As a result, a total of 166 articles were analyzed, after elimination of papers written in languages other than English or not directly related to the selected topics. Conclusion:: Machine learning algorithms are computer-based statistical approaches which can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.

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Machine Learning Approaches: From Theory to Application in Schizophrenia

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/867924 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 37

Author(s):

Elisa Veronese ◽

Umberto Castellani ◽

Denis Peruzzo ◽

Marcella Bellani ◽

Paolo Brambilla

Keyword(s):

Machine Learning ◽

Pattern Recognition ◽

Support Vector Machine ◽

Clinical Practice ◽

Disease Diagnosis ◽

Support Vector ◽

Healthy Controls ◽

Learning Approaches ◽

Neuroimaging Data ◽

Comparison Of The Results

In recent years, machine learning approaches have been successfully applied for analysis of neuroimaging data, to help in the context of disease diagnosis. We provide, in this paper, an overview of recent support vector machine-based methods developed and applied in psychiatric neuroimaging for the investigation of schizophrenia. In particular, we focus on the algorithms implemented by our group, which have been applied to classify subjects affected by schizophrenia and healthy controls, comparing them in terms of accuracy results with other recently published studies. First we give a description of the basic terminology used in pattern recognition and machine learning. Then we separately summarize and explain each study, highlighting the main features that characterize each method. Finally, as an outcome of the comparison of the results obtained applying the described different techniques, conclusions are drawn in order to understand how much automatic classification approaches can be considered a useful tool in understanding the biological underpinnings of schizophrenia. We then conclude by discussing the main implications achievable by the application of these methods into clinical practice.

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