scholarly journals A Review on Machine Learning Techniques to Predict Diseases

2019 ◽  
pp. 525-529
Author(s):  
Rohit A Nitnaware ◽  
Prof. Vijaya Kamble
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
The Past ◽  
Course Of Action ◽  
Learning Techniques ◽  
Past Data ◽  
Game Plan

In Disease Diagnosis, affirmation of models is so basic for perceiving the disease exactly. Machine learning is the field, which is used for building the models that can predict the yield relies upon the wellsprings of data, which are connected subject to the past data. Disease unmistakable verification is the most essential task for treating any disease. Classification computations are used for orchestrating the disease. There are a couple of classification computations and dimensionality decline counts used. Machine Learning empowers the PCs to learn without being changed remotely. By using the Classification Algorithm, a hypothesis can be looked over the course of action of decisions the best fits a game plan of recognition. Machine Learning is used for the high dimensional and the multi-dimensional data. Better and modified computations can be made using Machine Learning.

scholarly journals Recent Development of Artificial Intelligence Applied in Echocardiography: A Review (Preprint)

2019 ◽  
Author(s):  
Lu Liu ◽  
Ahmed Elazab ◽  
Baiying Lei ◽  
Tianfu Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Tissue Segmentation ◽  
The Past ◽  
Learning Techniques ◽  
The Future

BACKGROUND Echocardiography has a pivotal role in the diagnosis and management of cardiovascular diseases since it is real-time, cost-effective, and non-invasive. The development of artificial intelligence (AI) techniques have led to more intelligent and automatic computer-aided diagnosis (CAD) systems in echocardiography over the past few years. Automatic CAD mainly includes classification, detection of anatomical structures, tissue segmentation, and disease diagnosis, which are mainly completed by machine learning techniques and the recent developed deep learning techniques. OBJECTIVE This review aims to provide a guide for researchers and clinicians on relevant aspects of AI, machine learning, and deep learning. In addition, we review the recent applications of these methods in echocardiography and identify how echocardiography could incorporate AI in the future. METHODS This paper first summarizes the overview of machine learning and deep learning. Second, it reviews current use of AI in echocardiography by searching literature in the main databases for the past 10 years and finally discusses potential limitations and challenges in the future. RESULTS AI has showed promising improvements in analysis and interpretation of echocardiography to a new stage in the fields of standard views detection, automated analysis of chamber size and function, and assessment of cardiovascular diseases. CONCLUSIONS Compared with machine learning, deep learning methods have achieved state-of-the-art performance across different applications in echocardiography. Although there are challenges such as the required large dataset, AI can provide satisfactory results by devising various strategies. We believe AI has the potential to improve accuracy of diagnosis, reduce time consumption, and decrease the load of cardiologists.

scholarly journals LITERATURE SURVEY ON MACHINE LEARNING BASED TECHNIQUES IN MEDICAL DATAANALYSIS

2019 ◽  
pp. 1-4
Author(s):  
Lavanya Vemulapalli
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Intelligent Algorithms ◽  
The Past ◽  
Disease Prognosis ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Comprehensive Survey

Machine Learning plays a significant role among the areas of Artificial Intelligence (AI). During recent years, Machine Learning (ML) has been attracting many researchers, and it has been successfully applied in many fields such as medical, education, forecasting etc., Right now, the diagnosis of diseases is mostly from expert's decision. Diagnosis is a major task in clinical science as it is crucial in determining if a patient is having the disease or not. This in turn decides the suitable path of treatment for disease diagnosis. Applying machine learning techniques for disease diagnosis using intelligent algorithms has been a hot research area of computer science. This paper throws a light on the comprehensive survey on the machine learning applications in the medical disease prognosis during the past decades

scholarly journals CONCEPT OF SENTIMENT ANALYSIS USING MACHINE LEARNING TECHNIQUES

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Shuchita Mudgil ◽  
Prof Ashok Verma
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Machine Learning Techniques ◽  
Emotional States ◽  
The Past ◽  
Learning Techniques ◽  
Past Data

Sentiment analysis is used to conclude the approach of a consumer with respect to some topic. Sentimental analysis, a sub discipline within data mining and computational linguistics, refers to the methodology for mining, understanding the opinions expressed by the consumer in various forms like forums, forms blogs etc. The goal of sentiment analysis is to identify emotional states in online text. We Know human’s learns from past knowledge and machines follows instructions given by humans. But what if humans can prepare the machines from the past data and to put output to work much faster well that what is machine learning is it’s not about learning it’s also about understanding. So we will learn about analysis of sentiments using machine learning techniques

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

Implementation of machine learning techniques for disease diagnosis

2021 ◽  
Author(s):  
Shachi Mall ◽  
Ashutosh Srivastava ◽  
Bireshwar Dass Mazumdar ◽  
Manmohan Mishra ◽  
Sunil L. Bangare ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Learning Techniques
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