Development of Big Data Predictive Analytics Model for Disease Prediction using Machine learning Technique

2019 ◽  
Vol 43 (8) ◽  
Author(s):  
R. Venkatesh ◽  
C. Balasubramanian ◽  
M. Kaliappan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Predictive Analytics ◽  
Disease Prediction ◽  
Machine Learning Technique ◽  
Learning Technique

New recommendation to predict export value using big data and machine learning technique

2021 ◽  
pp. 1-14
Author(s):  
Rani Nooraeni ◽  
Jimmy Nickelson ◽  
Eko Rahmadian ◽  
Nugroho Puspito Yudho
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Big Data ◽  
Ann Model ◽  
Machine Learning Technique ◽  
Current Period ◽  
Learning Technique ◽  
Monthly Period ◽  
Conventional Machine ◽  
Artificial Neural Network Ann

Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model.

scholarly journals An Improved Machine Learning Technique with Effective Heart Disease Prediction System

2021 ◽  
Vol 69 (3) ◽  
pp. 4169-4181
Author(s):  
Mohammad Tabrez Quasim ◽  
Saad Alhuwaimel ◽  
Asadullah Shaikh ◽  
Yousef Asiri ◽  
Khairan Rajab ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Prediction ◽  
Prediction System ◽  
Machine Learning Technique ◽  
Learning Technique

Heart Disease Prediction Using Machine Learning Technique

2021 ◽  
pp. 193-201
Author(s):  
Priyanka P. Pattnaik ◽  
Soumya Ranjan Padhy ◽  
Bhabani Shankar Prasad Mishra ◽  
Subhashree Mishra ◽  
Pradeep Kumar Mallick
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Prediction ◽  
Machine Learning Technique ◽  
Learning Technique

scholarly journals Application of Machine Learning in Rhinology: A State of the Art Review

2020 ◽  
Vol 63 (8) ◽  
pp. 341-349
Author(s):  
Myeong Sang Yu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Medical Records ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Learning Technique ◽  
Machine Learning Applications

The revolutionary development of artificial intelligence (AI) such as machine learning and deep learning have been one of the most important technology in many parts of industry, and also enhance huge changes in health care. The big data obtained from electrical medical records and digitalized images accelerated the application of AI technologies in medical fields. Machine learning techniques can deal with the complexity of big data which is difficult to apply traditional statistics. Recently, the deep learning techniques including convolutional neural network have been considered as a promising machine learning technique in medical imaging applications. In the era of precision medicine, otolaryngologists need to understand the potentialities, pitfalls and limitations of AI technology, and try to find opportunities to collaborate with data scientists. This article briefly introduce the basic concepts of machine learning and its techniques, and reviewed the current works on machine learning applications in the field of otolaryngology and rhinology.

SOM approach for clustering customers using credit card transactions

2019 ◽  
Vol 12 (3) ◽  
pp. 372-388
Author(s):  
Seda Yanık ◽  
Abdelrahman Elmorsy
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Credit Card ◽  
Consumption Patterns ◽  
Self Organizing Map ◽  
Machine Learning Technique ◽  
Data Set ◽  
Number Of Clusters ◽  
Content Type ◽  
Learning Technique

Purpose The purpose of this paper is to generate customer clusters using self-organizing map (SOM) approach, a machine learning technique with a big data set of credit card consumptions. The authors aim to use the consumption patterns of the customers in a period of three months deducted from the credit card transactions, specifically the consumption categories (e.g. food, entertainment, etc.). Design/methodology/approach The authors use a big data set of almost 40,000 credit card transactions to cluster customers. To deal with the size of the data set and the eliminated the required parametric assumptions the authors use a machine learning technique, SOMs. The variables used are grouped into three as demographical variables, categorical consumption variables and summary consumption variables. The variables are first converted to factors using principal component analysis. Then, the number of clusters is specified by k-means clustering trials. Then, clustering with SOM is conducted by only including the demographical variables and all variables. Then, a comparison is made and the significance of the variables is examined by analysis of variance. Findings The appropriate number of clusters is found to be 8 using k-means clusters. Then, the differences in categorical consumption levels are investigated between the clusters. However, they have been found to be insignificant, whereas the summary consumption variables are found to be significant between the clusters, as well as the demographical variables. Originality/value The originality of the study is to incorporate the credit card consumption variables of customers to cluster the bank customers. The authors use a big data set and dealt with it with a machine learning technique to deduct the consumption patterns to generate the clusters. Credit card transactions generate a vast amount of data to deduce valuable information. It is mainly used to detect fraud in the literature. To the best of the authors’ knowledge, consumption patterns obtained from credit card transaction are first used for clustering the customers in this study.

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