Shubhangi Dc, BasavarajGadgay, Nuzhat Fatima, M. A. Waheed
{"title":"基于机器学习的揭示心理不稳定","authors":"Shubhangi Dc, BasavarajGadgay, Nuzhat Fatima, M. A. Waheed","doi":"10.1109/ICETEMS56252.2022.10093279","DOIUrl":null,"url":null,"abstract":"In today’s world, people are experiencing behavioral - and mental illnesses as a consequence of increased stress and pressure in their everyday lives. Anxiety, depression, stress, schizophrenia, and bipolar disorder are just a few instances of mental health issues. Mental disease is accompanied by both physical and emotional symptoms. Based on their actions and thoughts, this study will establish whether or not a person is suffering from mental illness. Panic attacks, sweating, palpitations, sadness, concern, overthinking, mental illness is indicated by symptoms such as delusions and hallucinations, and each symptom represents a different form of mental disorder. Five machine learning methods were utilized for this study: XGBoost, SVM, Logistic Regression, and Decision Tree, KNN. We employed a feature selection strategy that incorporated an additional tree classifier as well as other pre-processing techniques in this work. A machine learning algorithm has been utilized to identify a mental illness based on the symptoms of a patient that use the feature extraction technique.Parameters Recall, Accuracy, Precision, and Fl-score were used to assess the efficacy of machine learning models.","PeriodicalId":170905,"journal":{"name":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning Based Revealing Psychology Destabilization\",\"authors\":\"Shubhangi Dc, BasavarajGadgay, Nuzhat Fatima, M. A. Waheed\",\"doi\":\"10.1109/ICETEMS56252.2022.10093279\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In today’s world, people are experiencing behavioral - and mental illnesses as a consequence of increased stress and pressure in their everyday lives. Anxiety, depression, stress, schizophrenia, and bipolar disorder are just a few instances of mental health issues. Mental disease is accompanied by both physical and emotional symptoms. Based on their actions and thoughts, this study will establish whether or not a person is suffering from mental illness. Panic attacks, sweating, palpitations, sadness, concern, overthinking, mental illness is indicated by symptoms such as delusions and hallucinations, and each symptom represents a different form of mental disorder. Five machine learning methods were utilized for this study: XGBoost, SVM, Logistic Regression, and Decision Tree, KNN. We employed a feature selection strategy that incorporated an additional tree classifier as well as other pre-processing techniques in this work. A machine learning algorithm has been utilized to identify a mental illness based on the symptoms of a patient that use the feature extraction technique.Parameters Recall, Accuracy, Precision, and Fl-score were used to assess the efficacy of machine learning models.\",\"PeriodicalId\":170905,\"journal\":{\"name\":\"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICETEMS56252.2022.10093279\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Emerging Trends in Engineering and Medical Sciences (ICETEMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETEMS56252.2022.10093279","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine Learning Based Revealing Psychology Destabilization
In today’s world, people are experiencing behavioral - and mental illnesses as a consequence of increased stress and pressure in their everyday lives. Anxiety, depression, stress, schizophrenia, and bipolar disorder are just a few instances of mental health issues. Mental disease is accompanied by both physical and emotional symptoms. Based on their actions and thoughts, this study will establish whether or not a person is suffering from mental illness. Panic attacks, sweating, palpitations, sadness, concern, overthinking, mental illness is indicated by symptoms such as delusions and hallucinations, and each symptom represents a different form of mental disorder. Five machine learning methods were utilized for this study: XGBoost, SVM, Logistic Regression, and Decision Tree, KNN. We employed a feature selection strategy that incorporated an additional tree classifier as well as other pre-processing techniques in this work. A machine learning algorithm has been utilized to identify a mental illness based on the symptoms of a patient that use the feature extraction technique.Parameters Recall, Accuracy, Precision, and Fl-score were used to assess the efficacy of machine learning models.