Notice Board :

Call for Paper
Vol. 14 Issue 4

Submission Start Date:
May 01, 2022

Acceptence Notification Start:
May 10, 2022

Submission End:
May 20, 2022

Final MenuScript Due:
May 22, 2022

Publication Date:
May 25, 2021
                         Notice Board: Call for PaperVol. 14 Issue 4      Submission Start Date: May 01, 2022      Acceptence Notification Start: May 10, 2022      Submission End: May 20, 2022      Final MenuScript Due: May 22, 2022      Publication Date: May 25, 2021




Volume XIII Issue XII

Author Name
Sangeeta Kumari, Ritesh Kumar yadav, Varsha Namdeo
Year Of Publication
2021
Volume and Issue
Volume 13 Issue 12
Abstract
The medical field regularly handles enormous amounts of data. Handling huge data by conventional methods can affect the results. Algorithms for machine learning can be used to find out facts in medical research, in particular for disease prediction. The early recognition of disease is crucial for the analysis of patient medicines and specialists. Machine learning algorithms like Decision trees, Support vector machine, Multilayer perceptron, Bayes classifiers, K-Nearest Neighbors Ensemble classifier techniques etc are used to determine various ailments. Using machine learning algorithms can lead to rapid disease prediction with high accuracy. This research paper analyzes how machine learning techniques are used to predict different diseases and its types. This paper examined research papers focusing mainly on the prediction of chronic kidney disease, machine learning, heart disease, diabetes, and breast cancer. The paper also examines the hybrid approach that increases the performance o
PaperID
2021/EUSRM/12/2021/61246

Author Name
Priyadarshni Kumari, Chinmay Bhatt, Varsha Namdeo
Year Of Publication
2021
Volume and Issue
Volume 13 Issue 12
Abstract
Among the different causes of death, heart disease is highlighted as the most common. Due to medical practitioners' lack of knowledge and expertise about warning indications of heart failure, detecting heart illness might be difficult. In the healthcare industry, there is a vast amount of data. Early identification and prevention of heart-related disorders can be accomplished by employing the most effective data mining approaches. In the medical field, both machine learning (ML) and data mining (DM) techniques have proven to be useful and significant. The current study project's goal is to examine many risk parameters that have been highlighted in the analysis of heart disease, as well as to uncover multiple strategies for the detection and prediction of heart disease, as well as to evaluate the shortcomings of previous work. The article uses DM approaches to synthesize existing heart disease prediction studies, considering a variety of DM techniques to determine the most appropriate a
PaperID
2021/EUSRM/12/2021/61247