Application of Internet Assistance Computation for Disease Prediction and Bio-modeling: Modern Trends in Medical Science

2019 ◽  
pp. 327-346 ◽  
Author(s):  
Manojit Bhattacharya ◽  
Avijit Kar ◽  
Ramesh Chandra Malick ◽  
Chiranjib Chakraborty ◽  
Basanta Kumar Das ◽  
...  
Keyword(s):  
Medical Science ◽  
Disease Prediction

scholarly journals Digital Wellness: A Smart Health Care System Using Machine Learning

2021 ◽  
Vol 10 (6) ◽  
pp. 5-11
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Algorithm ◽  
Medical Science ◽  
Medical Data ◽  
Accurate Prediction ◽  
Disease Prediction ◽  
Accurate Analysis ◽  
Disease Symptoms ◽  
Pattern Information

Nowadays,people face various diseases due to environmental condition and their living habits. So the prediction of disease at an earlier stage becomes an important task. But the accurate prediction based on symptoms becomes too difficult for the doctor. The correctprediction of disease is the most challenging task. To overcome this problem data mining plays an important role to predict the disease. Medical science has a large amount of data growth per year. Due to the increasing amount of data growth in the medicaland healthcare field the accurate analysis of medical data has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in a huge amount of medical data. We proposed general disease prediction based on the symptoms of the patient. For the disease prediction, we use Convolutional neural network (CNN) machine learning algorithm for the accurate prediction of disease. For disease prediction required disease symptoms dataset. After general disease prediction, this system able to gives the risk associated with a general disease which is a lower risk of general disease or highe

Machine Learning Approaches for Cardiovascular Disease Prediction

2021 ◽  
pp. 75-84
Author(s):  
Stuti Pandey ◽  
Abhay Kumar Agarwal
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Human Life ◽  
Medical Science ◽  
Second Primary ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Prediction Systems ◽  
Medical Analysis

In a human body, the heart is the second primary organ after the brain. It causes either a long-term impairment or death of a person if suffering from a cardiovascular disease. In medical science, a proper medical analysis and examination of a cardiovascular disease is very crucial, convincing, and sophisticated task for saving a human life. Data analytics rises because of the absence of sufficient practical tools for exploring the trends and unknown relationships in e-health records. It predicts and achieves information which can ease the diagnosis. This survey examines cardiovascular disease prediction systems developed by different researchers. It also reviews the trend of machine learning approaches used in the past decade with results. Related studies comprise the performance of various classifiers on distinct datasets.

scholarly journals A multi-layer perceptron based improved thyroid disease prediction system

2020 ◽  
Vol 17 (1) ◽  
pp. 524
Author(s):  
Arvind Selwal ◽  
Ifrah Raoof
Keyword(s):  
Multilayer Perceptron ◽  
Thyroid Disease ◽  
Gradient Descent ◽  
Biochemical Analysis ◽  
Medical Science ◽  
Disease Prediction ◽  
Prediction System ◽  
Multi Layer Perceptron ◽  
Proposed Model ◽  
Lower Accuracy

<p>A challenging task for the medical science is to achieve the accurate diagnosis of diseases prior to its treatment. A pattern classifier is used for solving complex and non-separable computing problems in different fields like biochemical analysis, image processing and chemical analysis etc .The accuracy for thyroid diagnosis system may be improved by considering few additional attributes like heredity ,age, anti-bodies etc.  In this paper, a thyroid disease prediction system is developed using multilayer perceptron (MLP). The proposed system uses 7–11 attributes of individuals to classify them in normal, hyperthyroid and hypothyroid classes. The proposed model uses gradient descent backpropogation algorithm for training the multilayer perceptron using dataset of 120 subjects. The thyroid prediction system promises excellent overall accuracy of ~100% for 11 attributes. However, the system results in a lower accuracy of 66.7% using 11 attributes and 70% using 7 attributes with 30 subjects.</p>

scholarly journals A Hybrid System to Improve the Performance of Diabetes Disease Prediction using Genetic Algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 1720-1726
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Mortality Rate ◽  
Decision Tree ◽  
Prediction Model ◽  
Naive Bayes ◽  
Medical Science ◽  
Naïve Bayes ◽  
Support Vector ◽  
Disease Prediction

Currently, data mining is playing a significant role in the healthcare system. It helps to extract the hidden pattern from the clinical dataset for further analysis. Also, it can be used to build a tool to manage the medical management system. Among the life-threatening diseases, diabetes mellitus is treated as a serious disease worldwide. Due to its mortality rate, early prediction and diagnosis are very important. Several research works are going on the mentioned issues to reduce the complications caused by diabetes as well as the mortality rate. The medical science needs to analyze an enormous quantity of clinical data for diagnosis purposes using machine learning techniques. In recent approaches, the disease datasets may contain insignificant and digressive features causing less accurate results. The aim of this paper is to analyze the existing prediction systems and hence develop a hybrid disease prediction model using the Genetic Algorithm for Naïve Bayes, Decision Tree and Support Vector Machine classifiers for better accuracy. This proposed diabetes prediction model produces the accuracies of 0.8182, 0.8052, and 0.8312 when Naïve Bayes, Decision Tree, and Support Vector Machine classifiers are used respectively. From the experimental results, it can be demonstrated that for all cases Support Vector Machine provides higher accuracy comparing to the other classifiers. In the analysis, the Pima Indian diabetes dataset is used to construct the proposed model.

scholarly journals Heart Diseases Prediction using Deep Learning Neural Network Model

2020 ◽  
Vol 9 (3) ◽  
pp. 2244-2248
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Heart Diseases ◽  
Medical Science ◽  
Disease Prediction ◽  
Health Issues ◽  
Deep Learning Neural Network

Deep learning plays an important role in the field of medical science in solving health issues and diagnosing various diseases. So in this paper, we will discuss heart disease. We proposed a model for heart disease prediction. Heart Disease is on of key area where Deep Neural Network can be used so we can improve the overall quality of the classification of heart disease. The classification can be performed on the various ways like KNN, SVM, Naïve Bayes, Random Forest. Heart Disease UCI dataset will be used to demonstrate Talos Hyper-parameter optimization is more efficient than others.

Spiritual Healing, Sick Kids and the Law: Inequities in the American Healthcare System

2003 ◽  
Vol 29 (2-3) ◽  
pp. 269-299
Author(s):  
Janna C. Merrick
Keyword(s):  
Medical Science ◽  
Spiritual Healing ◽  
Best Interests ◽  
Legal Rights ◽  
High Tech ◽  
Divine Love ◽  
Religious Minority ◽  
Human Need ◽  
East End ◽  
The Church

Main Street in Sarasota, Florida. A high-tech medical arts building rises from the east end, the county's historic three-story courthouse is two blocks to the west and sandwiched in between is the First Church of Christ, Scientist. A verse inscribed on the wall behind the pulpit of the church reads: “Divine Love Always Has Met and Always Will Meet Every Human Need.” This is the church where William and Christine Hermanson worshipped. It is just a few steps away from the courthouse where they were convicted of child abuse and third-degree murder for failing to provide conventional medical care for their seven-year-old daughter.This Article is about the intersection of “divine love” and “the best interests of the child.” It is about a pluralistic society where the dominant culture reveres medical science, but where a religious minority shuns and perhaps fears that same medical science. It is also about the struggle among different religious interests to define the legal rights of the citizenry.

Left Ventricular Hypertrophy

2014 ◽  
Vol 19 (2) ◽  
pp. 11-15
Author(s):  
Steven L. Demeter
Keyword(s):  
Risk Factors ◽  
Left Ventricular Hypertrophy ◽  
Ventricular Hypertrophy ◽  
Left Ventricular Mass Index ◽  
Medical Science ◽  
Careful Analysis ◽  
Left Ventricular ◽  
Hypertensive Disease ◽  
Accurate Analysis ◽  
Clear Explanation

Abstract The fourth, fifth, and sixth editions of the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides) use left ventricular hypertrophy (LVH) as a variable to determine impairment caused by hypertensive disease. The issue of LVH, as assessed echocardiographically, is a prime example of medical science being at odds with legal jurisprudence. Some legislatures have allowed any cause of LVH in a hypertensive individual to be an allowed manifestation of hypertensive changes. This situation has arisen because a physician can never say that no component of LVH was not caused by the hypertension, even in an individual with a cardiomyopathy or valvular disorder. This article recommends that evaluators consider three points: if the cause of the LVH is hypertension, is the examinee at maximum medical improvement; is the LVH caused by hypertension or another factor; and, if apportionment is allowed, then a careful analysis of the risk factors for other disorders associated with LVH is necessary. The left ventricular mass index should be present in the echocardiogram report and can guide the interpretation of the alleged LVH; if not present, it should be requested because it facilitates a more accurate analysis. Further, if the cause of the LVH is more likely independent of the hypertension, then careful reasoning and an explanation should be included in the impairment report. If hypertension is only a partial cause, a reasoned analysis and clear explanation of the apportionment are required.

Medical science in crime detection

JAMA ◽  
1967 ◽  
Vol 200 (2) ◽  
pp. 155-160
Keyword(s):  
Medical Science ◽  
Crime Detection
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