High-Performance Disease Prediction and Recommendation Generation Healthcare System Using I3 Algorithm

2021 ◽  
pp. 41-52
Author(s):  
P. J. Sathish Kumar ◽  
V. Auxilia Osvin Nancy ◽  
N. Sathish ◽  
K. Kajendran ◽  
N. Pugazhendi ◽  
...  
Keyword(s):  
Healthcare System ◽  
High Performance ◽  
Disease Prediction

Disease Prediction using Naïve Bayes - Machine Learning Algorithm

2021 ◽  
Vol 6 (4) ◽  
pp. 17-22
Author(s):  
Chandrasekhar Rao Jetti ◽  
Rehamatulla Shaik ◽  
Sadhik Shaik
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Healthcare System ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Disease Prediction ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques ◽  
Smart Healthcare

It can occur on many occasions that you or a loved one requires urgent medical assistance, but they are unavailable due to unforeseen circumstances, or that we are unable to locate the appropriate doctor for the care. As a result, we will try to incorporate an online intelligent Smart Healthcare System in this project to solve this issue. It's a web-based programmed that allows patients to get immediate advice about their health problems. The aim of the smart healthcare system is to create a web application that can take a user's symptoms and predict diseases, as well as serve as an online consultant for various diseases. We created an expert system called Smart Health Care System, which is used to make doctors' jobs easier. A machine examines a patient at a basic level and recommends diseases that may be present. It begins by inquiring about the patient's symptoms; if the device is able to determine the relevant condition, it then recommends a doctor in the patient's immediate vicinity. The system will show the result based on the available accumulated data. We're going to use some clever data mining techniques here. We use several intelligent data mining techniques to guess the most accurate illness that could be associated with a patient's symptoms, and we use an algorithm (Naive Bayes) to map the symptoms with potential diseases based on a database of many patients' medical records. This system not only makes doctors' jobs easier, but it also benefits patients by getting them the care they need as soon as possible. Keywords: Disease Prediction, Naïve Bayes, Machine Learning Algorithm, Smart Healthcare System.

scholarly journals Implementation of Lung Cancer Diagnosis based on DNN in Healthcare System

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 798-812
Author(s):  
Suhiar Mohammed Zeki Abd Alsammed
Keyword(s):  
Lung Cancer ◽  
Healthcare System ◽  
Cancer Diagnosis ◽  
Association Rule ◽  
High Performance ◽  
Classification Performance ◽  
Cancer Dataset ◽  
Lung Cancer Diagnosis ◽  
Zero Values ◽  
Class Association Rule

Cancer represents a kind of disease that is widespread throughout the world. Actually, there are several kinds of cancer. However, lung cancer represents the most prevalent cancer form and can lead to death with late healthcare. Therefore, it is essential to initialize therapy via diagnosing lung cancer for decreasing the death chance. Classification is one of the fundamental issues in the knowledge discovery fields and scientific decisions. There are many types of techniques used for constructing classifiers and cancer diagnosis. Recently, deep learning becomes a powerful and popular classification technique for many areas of medical data diagnosis in the healthcare systems. In this paper, an effective and accurate deep neural network (DNN) based lung cancer diagnosis implemented in the healthcare system has been proposed which includes three main phases; pre-processing, generating strong rules, and classification. The input data are pre-processed in the first phase. Because these data are entered from databases, so there are missing data that should be replaced with zero values. Then, data are normalized for speeding up the learning phase. After that, the class association rule is used to enhance the classification performance by generating frequent patterns inducible from the dataset which include features that are significant to the class attribute. Finally, DNN is utilized in the process of classification for obtaining a sample diagnosis estimate. DNN based diagnosis system was tested and evaluated on the lung cancer dataset which has 25 attributes and 1000 instances. The obtained results demonstrated that the proposed system achieved a high performance compared to other existing lung cancer diagnosis systems with 95% accuracy, 97% specificity, and 95% sensitivity.

scholarly journals A Model for Coronary Heart Disease Prediction Using Data Mining Classification Techniques

2019 ◽  
pp. 1-19
Author(s):  
Dominic Obwogi Makumba ◽  
Wilson Cheruiyot ◽  
Kennedy Ogada
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
High Performance ◽  
Model Comparison ◽  
Confusion Matrix ◽  
Well Being ◽  
Performance Measure ◽  
Treatment Preferences ◽  
Disease Prediction ◽  
Classification Techniques

Nowadays the guts malady is one amongst the foremost causes of death within the world. Thus it s early prediction and diagnosing is vital in medical field, which might facilitate in on time treatment, decreasing health prices and decreasing death caused by it. The treatment values the disease is not cheap by most of the patients and Clinical choices are usually raised supported by doctors‟ intuition and skill instead of on the knowledge-rich information hidden within the stored data. The model  for prediction of heart disease using a classification techniques in data mining reduce medical errors, decreases unwanted exercise variation, enhance patient well-being and improves patient results. The model has been developed to support decision making in heart disease prediction based on data mining techniques. The experiments were performed using the model, based on the three techniques, and their accuracy in prediction noted. The decision tree, naïve Bayes, KNN (K-Nearest Neighbors) and WEKA API (Waikato Environment for Knowledge Analysis-application programming interface) were the various data mining methods that were used. The model predicts the likelihood of getting a heart disease using more input medical attributes. 13 attributes that is: blood pressure, sex, age, cholesterol, blood sugar among other factors such as genetic factors, sedentary behavior, socio-economic status and race has been use to predict the likelihood of patient getting a Heart disease until now. This study research added two more attributes that is: Obesity and Smoking.740 Record sets with medical attributes was obtained from a publicly available database for heart disease from machine learning repository with the help of the datasets, and the patterns significant to the heart attack prediction was extracted and divided into two data sets, one was used for training which consisted of 296 records & another for testing consisted of 444 records, and the fraction of accuracy of every data mining classification that was applied was used as standard for performance measure. The performance was compared by calculating the confusion matrix that assists to find the precision recall and accuracy. High performance and accuracy was provided by the complete system model. Comparison between the proposed techniques and the existing one in the prediction capability was presented. The model system assists clinicians in survival rate prediction of an individual patient and future medication is planned for. Consequently, the families, relatives, and their patients can plan for treatment preferences and plan for their budget consequently.

Encouraging participation in health system reform: is clinical engagement a useful concept for policy and management?

2012 ◽  
Vol 36 (4) ◽  
pp. 378 ◽  
Author(s):  
Dimitra Bonias ◽  
Sandra G. Leggat ◽  
Timothy Bartram
Keyword(s):  
Public Health ◽  
Health System ◽  
Employee Engagement ◽  
Healthcare System ◽  
High Performance ◽  
Health Sector ◽  
System Reform ◽  
Health System Reform ◽  
Australian Health ◽  
Clinical Engagement

Objective. Recent health system enquiries and commissions, including the National Health and Hospital Reform Commission, have promoted clinical engagement as necessary for improving the Australian healthcare system. In fact, the Rudd Government identified clinician engagement as important for the success of the planned health system reform. Yet there is uncertainty about how clinical engagement is understood in health policy and management. This paper aims to clarify how clinical engagement is defined, measured and how it might be achieved in policy and management in Australia. Methods. We review the literature and consider clinical engagement in relation to employee engagement, a defined construct within the management literature. We consider the structure and employment relationships of the public health sector in assessing the relevance of this literature. Conclusions. Based on the evidence, we argue that clinical engagement is similar to employee engagement, but that engagement of clinicians who are employees requires a different construct to engagement of clinicians who are independent practitioners. The development of this second construct is illustrated using the case of Visiting Medical Officers in Victoria. Implications. Antecedent organisational and system conditions to clinical engagement appear to be lacking in the Australian public health system, suggesting meaningful engagement will be difficult to achieve in the short-term. This has the potential to threaten proposed reforms of the Australian healthcare system. What is known about the topic? Engagement of clinicians has been identified as essential for improving quality and safety, as well as successful health system reform, but there is little understanding of how to define and measure this engagement. What does this paper add? Clinical engagement is defined as the cognitive, emotional and physical contribution of health professionals to their jobs, and to improving their organisation and their health system within their working roles in their employing health service. While this construct applies to employees, engagement of independent practitioners is a different construct that needs to recognise out-of-role requirements for clinicians to become engaged in organisational and system reform. What are the implications for practitioners? This paper advances our understanding of clinical engagement, and suggests that based on research on high performance work systems, the Australian health system has a way to go before the antecedents of engagement are in place.

An Effective and Efficient Heart Disease Prediction Model Using Distributed High Performance Light GBM

2021 ◽  
Author(s):  
B.V. Chowdary ◽  
Jaina Kedarnath ◽  
Rachamalla Vyshnavi ◽  
Valluri Lavakush ◽  
Chavula Shashidhar
Keyword(s):  
Heart Disease ◽  
Prediction Model ◽  
High Performance ◽  
Disease Prediction

A High Performance Energy Analyzer for Use in Electron Scanning Microscopy

1969 ◽  
Vol 27 ◽  
pp. 14-15
Author(s):  
A. V. Crewe ◽  
M. Isaacson ◽  
D. Johnson
Keyword(s):  
High Performance ◽  
Vertical Plane ◽  
Median Plane ◽  
Electron Gun ◽  
Uniform Field ◽  
Loss Mechanism ◽  
Electron Scanning Microscopy ◽  
Sector Type ◽  
Double Focusing ◽  
Electron Energy Loss

A double focusing magnetic spectrometer has been constructed for use with a field emission electron gun scanning microscope in order to study the electron energy loss mechanism in thin specimens. It is of the uniform field sector type with curved pole pieces. The shape of the pole pieces is determined by requiring that all particles be focused to a point at the image slit (point 1). The resultant shape gives perfect focusing in the median plane (Fig. 1) and first order focusing in the vertical plane (Fig. 2).

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