scholarly journals Medical and Health Data Classification Method Based on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Yu Zeng ◽  
Fuchao Cheng
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Data Classification ◽  
Health Data ◽  
Medical Data ◽  
Technical Support ◽  
Classification Method ◽  
Medical Data Classification ◽  
Medical Health

The information defined in medical health data is researched based on machine learning-related algorithms. Also, this paper used random forest and other related algorithms to perform health data training and fitting. Research shows that the algorithm proposed in the paper can improve the progress of health data classification. The algorithm can provide technical support for the improvement of medical data classification.

scholarly journals A Framework for Medical Data Analysis using Deep Learning based on Conventional Neural Network (CNN) and Variable Auto-Encoder

2019 ◽  
Vol 8 (3) ◽  
pp. 852-857
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Data Classification ◽  
Medical Data ◽  
Complex Task ◽  
Medical Data Classification ◽  
High Level ◽  
Performance Results

Medical data classification is an important and complex task. Due to the nature of data, the data is in different forms like text, numeric, images and sometimes combination of all. The goal of this paper is to provide a high-level introduction into practical machine learning for purposes of medical data classification. In this paper we use CNN-Auto encoder to extract data from the medical repository and made the classification of heterogeneous medical data. Here Auto encoder uses to get the prime features and CNN is there to extract detailed features. Combination of these two mechanisms are more suitable for medical data classification. Hybrid AE-CNN (auto encoder based Convolutional neural network). Here the performance of proposed mechanism with respect to baseline methods will be assessed. The performance results showed that the proposed mechanism performed well.

Sparse Matrix Approach in Neural Networks for Effective Medical Data Sets Classifications

2020 ◽  
Vol 6 (2) ◽  
pp. 90-97
Author(s):  
Sagir Masanawa ◽  
Hamza Abubakar
Keyword(s):  
Intelligent System ◽  
Sparse Matrix ◽  
Data Classification ◽  
Medical Data ◽  
Data Sets ◽  
Matrix Approach ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Intelligent System ◽  
Medical Data Classification

In this paper, a hybrid intelligent system that consists of the sparse matrix approach incorporated in neural network learning model as a decision support tool for medical data classification is presented. The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners to accelerate diagnosis and treatment processes. The sparse matrix approach incorporated in neural network learning algorithm for scalability, minimize higher memory storage capacity usage, enhancing implementation time and speed up the analysis of the medical data classification problem. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. The proposed intelligent classification system maximizes the intelligently classification of medical data and minimizes the number of trends inaccurately identified. To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Hepatitis, SPECT Heart and Cleveland Heart from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity. The results were analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system was effective in undertaking medical data classification tasks.

Hybrid mechanism for medical data classification

2021 ◽  
Vol 19 (3/4) ◽  
pp. 243
Author(s):  
Ahelam Mainoddin Tikotikar ◽  
Mallikarjun M. Kodabagi
Keyword(s):  
Data Classification ◽  
Medical Data ◽  
Hybrid Mechanism ◽  
Medical Data Classification
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