scholarly journals Design and Application of a Smart Diagnostic System for Parkinson’s Patients using Machine Learning

2019 ◽  
Vol 10 (6) ◽  
Author(s):  
Asma Channa ◽  
Attiya Baqai ◽  
Rahime Ceylan
Keyword(s):  
Machine Learning ◽  
Diagnostic System ◽  
Design And Application

scholarly journals CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 617
Author(s):  
Umer Saeed ◽  
Young-Doo Lee ◽  
Sana Ullah Jan ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Context Aware ◽  
Sensor Faults ◽  
Training Time ◽  
Low Intensity ◽  
Fault Diagnostic

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

scholarly journals Respiratory Diseases Detection using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 2698-2701
Author(s):  
Shubham Shitole
Keyword(s):  
Machine Learning ◽  
Ct Scan ◽  
Lung Diseases ◽  
Respiratory Diseases ◽  
Diagnostic System ◽  
Extraction Process ◽  
Image Feature ◽  
Feature Selection Technique ◽  
Preprocessing Technique ◽  
Lengthy Process

Prediction of the Respiratory diseases in the earlier stage can be very useful specially to improve the survival rate of that patient. CT scan images are used to detect various lung diseases .These CT scan reports are sent to pathologists for further process. Pathologists analyze CT scan report and predict the infected tissues which are the main cause of the particular disease. This is lengthy process and to avoid this steps and increase the accuracy of the prediction Machine learning plays an important role . The system proposes to build "Predictive Diagnostic System" of infectious lung by using the concept of image processing in conjunction with machine learning. Proposed system will detect the disease from CT scan images and use preprocessing technique that will remove the noise and disturbance in image. Feature extraction process is applied to extract the useful features of underlying image, and feature selection technique will further optimize the top ranking features. CNN algorithm is then applied to classify the images for detection of Respiratory disease. After detection of disease, report will be generated and submitted to patient.

Application of Artificial Neural Networks for Heart Disease Prediction

2020 ◽  
Vol 17 (9) ◽  
pp. 4190-4196
Author(s):  
Kumar Suyash ◽  
K. R. Shobha
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Intelligent Systems ◽  
Heart Diseases ◽  
Medical Science ◽  
Back Propagation ◽  
Diagnostic System ◽  
Science Field ◽  
The World ◽  
Significant Life

Heart related diseases are on a rise throughout the world. While the WHO estimates 31% of all deaths worldwide are caused by heart related diseases, some estimates even attribute 18 million deaths throughout the world due to such diseases. Although, the monumental strides in the field of machine learning, especially neural networks have enabled us to solve complex recognition problems, we still at large have been unable to utilize their power to the maximum in the data rich medical science field. These networks can in fact be used to construct intelligent systems which can help predict the presence of heart diseases in their early stages. Such intelligent systems shall result in significant life savings due to the readily available timely medical care and the following treatments. Encompassing the techniques of classification, a supervised learning approach of machine learning, in these intelligent systems can be aimed at pinpointing the accurate diagnosis. This paper thus, proposes a diagnostic system for predicting the presence of heart diseases using neural networks with back propagation.

scholarly journals NOVEL APPROACHES TO DEVELOPMENT OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN THE LUNG CANCER DIAGNOSTICS

2019 ◽  
pp. 8-18 ◽  
Author(s):  
A. A. Meldo ◽  
L. V. Utkin ◽  
T. N. Trofimova ◽  
M. A. Ryabinin ◽  
V. M. Moiseenko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Neural Networks ◽  
Diagnostic System ◽  
Cancer Diagnostics ◽  
Point Of View ◽  
Training Data ◽  
Medical Community ◽  
Social Significance ◽  
Feature Dimension

The relevance of developing an intelligent automated diagnostic system (IADS) for lung cancer (LC) detection stems from the social significance of this disease and its leading position among all cancer diseases. Theoretically, the use of IADS is possible at a stage of screening as well as at a stage of adjusted diagnosis of LC. The recent approaches to training the IADS do not take into account the clinical and radiological classification as well as peculiarities of the LC clinical forms, which are used by the medical community. This defines difficulties and obstacles of using the available IADS. The authors are of the opinion that the closeness of a developed IADS to the «doctor’s logic» contributes to a better reproducibility and interpretability of the IADS usage results. Most IADS described in the literature have been developed on the basis of neural networks, which have several disadvantages that affect reproducibility when using the system. This paper proposes a composite algorithm using machine learning methods such as Deep Forest and Siamese neural network, which can be regarded as a more efficient approach for dealing with a small amount of training data and optimal from the reproducibility point of view. The open datasets used for training IADS include annotated objects which in some cases are not confirmed morphologically. The paper provides a description of the LIRA dataset developed by using the diagnostic results of St. Petersburg Clinical Research Center of Specialized Types of Medical Care (Oncology), which includes only computed tomograms of patients with the verified diagnosis. The paper considers stages of the machine learning process on the basis of the shape features, of the internal structure features as well as a new developed system of differential diagnosis of LC based on the Siamese neural networks. A new approach to the feature dimension reduction is also presented in the paper, which aims more efficient and faster learning of the system.

Machine learning approach for robot diagnostic system

2019 ◽  
Author(s):  
Nian-Ze Hu ◽  
Chih-Hui Simon Su ◽  
Cihun-Siyong Alex Gong ◽  
Cheng-Jung Lee ◽  
Yong-Sheng Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diagnostic System ◽  
Learning Approach ◽  
Machine Learning Approach

The Application of Machine Learning Technique for Malaria Diagnosis

2012 ◽  
pp. 2035-2043 ◽  
Author(s):  
C. Ugwu ◽  
N. L Onyejegbu ◽  
I. C Obagbuwa
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Knowledge Base ◽  
Healthcare Delivery ◽  
Diagnostic System ◽  
Malaria Diagnosis ◽  
Machine Learning Technique ◽  
Microsoft Access ◽  
Learning Technique ◽  
Health Organization

Healthcare delivery in African nations has long been a worldwide issue, which is why the United Nations and World Health Organization seek for ways to alleviate this problem and thereby reduce the number of lives that are lost every year due to poor health facilities and inadequate health care administration. Healthcare delivery concerns are most predominant in Nigeria and it became imperatively clear that the system of medical diagnosis must be automated. This paper explores the potential of machine learning technique (decision tree) in development of a malaria diagnostic system. The decision tree algorithm was used in the development of the knowledge base. Microsoft Access and Java programming language were used for database and user interfaces, respectively. During the diagnosis, symptoms are provided by the patient in the diagnostic system and a match is found in the knowledge base.

The Application of Machine Learning Technique for Malaria Diagnosis

2013 ◽  
pp. 263-272
Author(s):  
C. Ugwu ◽  
N. L Onyejegbu ◽  
I. C Obagbuwa
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Knowledge Base ◽  
Healthcare Delivery ◽  
Diagnostic System ◽  
Malaria Diagnosis ◽  
Machine Learning Technique ◽  
Microsoft Access ◽  
Learning Technique ◽  
Health Organization

Healthcare delivery in African nations has long been a worldwide issue, which is why the United Nations and World Health Organization seek for ways to alleviate this problem and thereby reduce the number of lives that are lost every year due to poor health facilities and inadequate health care administration. Healthcare delivery concerns are most predominant in Nigeria and it became imperatively clear that the system of medical diagnosis must be automated. This paper explores the potential of machine learning technique (decision tree) in development of a malaria diagnostic system. The decision tree algorithm was used in the development of the knowledge base. Microsoft Access and Java programming language were used for database and user interfaces, respectively. During the diagnosis, symptoms are provided by the patient in the diagnostic system and a match is found in the knowledge base.

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