Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States

Kumar, Sunil; Chong, Ilyoung

doi:10.3390/ijerph15122907

Cited by 143 publications

(74 citation statements)

References 56 publications

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“…To achieve the desired accuracy, it is essential to generate a reference for selecting the parameters that need to be recorded. The current study considers the use of different datasets as a useful method to ascertain the appropriate data that may have fewer variables and significant implications for predictions indeed [ 20 , 21 , 36 , 37 ]. So that the current study adopts the Spearman rank correlation coefficient approach in order to extract the best features, which is a commonly followed method to explore the relationships between attributes.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Arulmozhi

Basak

Sihalath

et al. 2021

Animals

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Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables; still, potential contributors that influence the homeostasis of livestock animals reared in closed barns. Further, predicting IAT and IRH encourages farmers to think ahead actively and to prepare the optimum solutions. Therefore, the primary objective of the current literature is to build and investigate extensive performance analysis between popular ML models in practice used for IAT and IRH predictions. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest regression (RFR), decision tree regression (DTR), and support vector regression (SVR) models were utilized for the prediction. This study used accessible factors such as external environmental data to simulate the models. In addition, three different input datasets named S1, S2, and S3 were used to assess the models. From the results, RFR models performed better results in both IAT (R2 = 0.9913; RMSE = 0.476; MAE = 0.3535) and IRH (R2 = 0.9594; RMSE = 2.429; MAE = 1.47) prediction among other models particularly with S3 input datasets. In addition, it has been proven that selecting the right features from the given input data builds supportive conditions under which the expected results are available. Overall, the current study demonstrates a better model among other models to predict IAT and IRH of a naturally ventilated swine building containing animals with fewer input attributes.

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Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Arulmozhi

Basak

Sihalath

et al. 2021

Animals

View full text Add to dashboard Cite

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“…Authors Kumar et al [27] in 2018 have tried to do correlation analysis over some selected datasets to enhance the accuracies when some classification problem arises.…”

Section: Proposed Solution Using Statistical Correlation Analysis Methodsmentioning

confidence: 99%

“…12 Statistical Correlation Analysis [27] Enhanced accuracy by using correlation analysis techniques.…”

mentioning

confidence: 99%

Machine Learning Concepts for Correlated Big Data Privacy

Biswas¹,

Khare²,

Agrawal³

et al. 2021

Preprint

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With data becoming a salient asset worldwide, dependence within data kept on growing, hence the real world datasets that one works upon in today's time are highly correlated. Since the past few years, researchers have given attention to this aspect of data privacy and found that where there exists a correlation among data, the existing privacy guarantees could not be assured with existing privacy algorithms. The privacy guarantees provided by existing algorithms were enough when there existed no relation between data in the datasets. Hence, by keeping the existence of data correlation into account, there is a dire need, to reconsider the privacy algorithms. Some of the research have considered to utilize a well known machine learning concept, i.e., Data Correlation Analysis to understand the relationship between data in a better way. This has given some promising results as well. Though its less but still a considerable amount of research has been done for correlated data privacy. But correlated big data privacy is very less explored. The real world datasets that are worked upon, are often large in size (technologically termed as big data) and house a high amount of data correlation. Hence, there is a grave need to understand and propose solutions for correlated big data privacy.

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“…So there is no compelling reason to keep every one of them. As they are the most likely agents of each other and utilizing a couple of them can ideally classify the data well [16]. The correlation plot of the features has been illustrated in Fig.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques

et al. 2021

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Cervical cancer growth is the fourth maximum of regular diseases in females. It is one of the sicknesses which is compromising ladies' wellbeing everywhere in the world and it is difficult to notice any sign in the beginning phase. But the screening process of cervical cancer sometimes is being hampered due to some social-behavioral factors. There is still a limited number of researches directed in cervical cancer identification dependent on the behavior and machine learning in the area of gynecology and computer science. In this research, we have proposed three machine learning models such as Decision Tree, Random Forest, and XGBoost to predict cervical cancer from behavior and its variables and we got significantly improved outcomes than the current methods with 93.33% accuracy. Moreover, we have shown the top features from the dataset according to the feature important scores to know their impacts on the development of the classification model.

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Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States

Cited by 143 publications

References 56 publications

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Machine Learning Concepts for Correlated Big Data Privacy

Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques

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