scholarly journals A Survey: Effective Machine Learning Based Classification Algorithm for Medical Dataset

2021 ◽  
Vol 3 (Special Issue 9S) ◽  
pp. 28-33
Author(s):  
Mahalakshmi G. ◽  
Shimaali Riyasudeen ◽  
Sairam R ◽  
Hari Sanjeevi R ◽  
Raghupathy B.
Keyword(s):  
Machine Learning ◽  
Classification Algorithm ◽  
Medical Dataset

Machine Learning Classification of Head Impact Sensor Data

2019 ◽  
Author(s):  
Tyler F. Rooks ◽  
Andrea S. Dargie ◽  
Valeta Carol Chancey
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
External Validation ◽  
Classification Algorithm ◽  
Sensor Data ◽  
Environmental Sensors ◽  
Head Acceleration ◽  
Machine Learning Classification ◽  
Environmental Sensor ◽  
Validation Set

Abstract A shortcoming of using environmental sensors for the surveillance of potentially concussive events is substantial uncertainty regarding whether the event was caused by head acceleration (“head impacts”) or sensor motion (with no head acceleration). The goal of the present study is to develop a machine learning model to classify environmental sensor data obtained in the field and evaluate the performance of the model against the performance of the proprietary classification algorithm used by the environmental sensor. Data were collected from Soldiers attending sparring sessions conducted under a U.S. Army Combatives School course. Data from one sparring session were used to train a decision tree classification algorithm to identify good and bad signals. Data from the remaining sparring sessions were kept as an external validation set. The performance of the proprietary algorithm used by the sensor was also compared to the trained algorithm performance. The trained decision tree was able to correctly classify 95% of events for internal cross-validation and 88% of events for the external validation set. Comparatively, the proprietary algorithm was only able to correctly classify 61% of the events. In general, the trained algorithm was better able to predict when a signal was good or bad compared to the proprietary algorithm. The present study shows it is possible to train a decision tree algorithm using environmental sensor data collected in the field.

Blind modulation classification algorithm based on machine learning for spatially correlated MIMO system

2017 ◽  
Vol 11 (7) ◽  
pp. 1000-1007 ◽  
Author(s):  
Xiaokai Liu ◽  
Chenglin Zhao ◽  
Pengbiao Wang ◽  
Yang Zhang ◽  
Tianpu Yang
Keyword(s):  
Machine Learning ◽  
Mimo System ◽  
Classification Algorithm ◽  
Modulation Classification ◽  
Spatially Correlated

scholarly journals Advanced Data Imputation Techniques for Predicting Type 2 Diabetes using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 4142-4149
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Missing Data ◽  
Early Stage ◽  
Research Work ◽  
Model Complexity ◽  
Bias Model ◽  
Proposed Model ◽  
Medical Dataset

Type 2 Diabetes mellitus is a serious metabolic disorder that is prevailing worldwide at an alarming rate. Medical dataset often suffers from the problem of missing data and outliers. However, handling of missing data with traditional mean based imputing may lead towards a bias model and return unpredictable outcome. Making complex models by combining multiple classifiers as well as some other methods could increase the accuracy which again is a time-consuming approach and requires heavy computation capability which significantly increases the deployment cost. The proposed research is to design a model to classify the data using class wise imputation technique and outlier handling. Performance of the proposed model is evaluated on nine machine learning classifiers and compared with traditional approaches like simple mean, median, and linear regression. Experimental results show the superiority of the proposed model in terms of classification accuracy and model complexity. The accuracy achieved by the proposed approach is 88.01%, which is highest as compared to the previous studies. The proposed research work is presented to improve accuracy, scalability and overall performance of the classification in the medical dataset, which ultimately proves to be a lifesaver if the diagnosis is achieved efficiently at an early stage.

scholarly journals Fake Job Detection and Analysis Using Machine Learning and Deep Learning Algorithms

2021 ◽  
Vol 11 (2) ◽  
pp. 642-650
Author(s):  
C.S. Anita ◽  
P. Nagarajan ◽  
G. Aditya Sairam ◽  
P. Ganesh ◽  
G. Deepakkumar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Cleaning ◽  
Personal Information ◽  
Learning Algorithms ◽  
Classification Algorithm ◽  
Paper Machine ◽  
Machine Learning Classification ◽  
Accuracy And Precision ◽  
Processing Step

With the pandemic situation, there is a strong rise in the number of online jobs posted on the internet in various job portals. But some of the jobs being posted online are actually fake jobs which lead to a theft of personal information and vital information. Thus, these fake jobs can be precisely detected and classified from a pool of job posts of both fake and real jobs by using advanced deep learning as well as machine learning classification algorithms. In this paper, machine learning and deep learning algorithms are used so as to detect fake jobs and to differentiate them from real jobs. The data analysis part and data cleaning part are also proposed in this paper, so that the classification algorithm applied is highly precise and accurate. It has to be noted that the data cleaning step is a very important step in machine learning project because it actually determines the accuracy of the machine learning as well as deep learning algorithms. Hence a great importance is emphasized on data cleaning and pre-processing step in this paper. The classification and detection of fake jobs can be done with high accuracy and high precision. Hence the machine learning and deep learning algorithms have to be applied on cleaned and pre-processed data in order to achieve a better accuracy. Further, deep learning neural networks are used so as to achieve higher accuracy. Finally all these classification models are compared with each other to find the classification algorithm with highest accuracy and precision.

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