ArticleInternational journal of environmental research and public health2018
Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States.
Article in International journal of environmental research and public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Impact of Demographic and Clinical Subgroups in Google Trends Data: Infodemiology Case Study on Asthma Hospitalizations.Journal of medical Internet research · 2025Article
- Smartphone sensor-based depression detection in campus environments: a proof-of-concept study with small-sample behavioral analysis.Frontiers in psychiatry · 2025Article
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- Machine learning based identification potential feature genes for prediction of drug efficacy in nonalcoholic steatohepatitis animal model.Lipids in health and disease · 2024Article
- Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study.World journal of hepatology · 2024Article
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- Explainable AI and optimized solar power generation forecasting model based on environmental conditions.PloS one · 2024Article
- Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data.BMC genomics · 2023Article
- Skin-Interfaced Wearable Sweat Sensors for Precision Medicine.Chemical reviews · 2023Review
- A multi-criteria decision-making (MCDM) approach for data-driven distance learning recommendations.Education and information technologies · 2023Article
- Editorial: Current development on wearable biosensors towards biomedical applications.Frontiers in bioengineering and biotechnology · 2023Article
- A novel multi-modal depression detection approach based on mobile crowd sensing and task-based mechanisms.Multimedia tools and applications · 2023Article
- Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics.ACS omega · 2022Article
- Gaming behavior and brain activation using functional near-infrared spectroscopy, Iowa gambling task, and machine learning techniques.Brain and behavior · 2022Article
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Correlation analysis is an extensively used technique that identifies interesting relationships in data. These relationships help us realize the relevance of attributes with respect to the target class to be predicted. This study has exploited correlation analysis and machine learning-based approaches to identify relevant attributes in the dataset which have a significant impact on classifying a patient's mental health status. For mental health situations, correlation analysis has been performed in Weka, which involves a dataset of depressive disorder symptoms and situations based on weather conditions, as well as emotion classification based on physiological sensor readings. Pearson's product moment correlation and other different classification algorithms have been utilized for this analysis. The results show interesting correlations in weather attributes for bipolar patients, as well as in features extracted from physiological data for emotional states.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.