Symptom Based Health Status Prediction via Decision Tree, KNN, XGBoost, LDA, SVM, and Random Forest

dc.authorscopusid58160836900
dc.authorscopusid57219906482
dc.contributor.authorMeriç, E.
dc.contributor.authorÖzer, Ç.
dc.date.accessioned2024-07-18T20:16:44Z
dc.date.available2024-07-18T20:16:44Z
dc.date.issued2023
dc.descriptionInternational Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022 -- 16 September 2022 through 17 September 2022 -- Kocaeli -- 291929en_US
dc.description.abstractMachine learning applications in health science become more important and necessary every day. With the help of these systems, the load of the medical staff will be lessened and faults because of a missing point, or tiredness will decrease. It should not be forgotten that the last decision lies with the professionals, and these systems will only help in decision-making. Predicting diseases with the help of machine learning algorithm can lessen the load of the medical staff. This paper proposes a machine learning model that analyzes healthcare data from a variety of diseases and shows the result from the best resulting algorithm in the model. It is aimed to have a system that facilitates the diagnosis of diseases caused by the density of data in the health field by using these algorithms of previously diagnosed symptoms, thus resulting in doctors going a faster way while diagnosing the disease and have a prediction about the diseases of people who do not have the condition to go to the hospital. In this way, it can ease the burden on health systems. The disease outcome corresponding to the 11 symptoms found in the data set used is previously experienced results. During the study, different ML algorithms such as Decision Tree, Random Forest, KNN, XGBoost, SVM, LDA were tried and compatibility/performance comparisons were made on the dataset used. The results are presented in a table. As a result of these comparisons and evaluations, it was seen that Random Forest Algorithm gave the best performance. While data was being processed, input parameters were provided to each model, and disease was taken as output. Within this limited resource, our model has reached an accuracy rate of 98%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-031-27099-4_15
dc.identifier.endpage207en_US
dc.identifier.isbn978-303127098-7
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85151056749en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage193en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-27099-4_15
dc.identifier.urihttps://hdl.handle.net/11411/6226
dc.identifier.volume643 LNNSen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDecision Tree (Dt)en_US
dc.subjectExtreme Gradient Boosting (Xgboost)en_US
dc.subjectGridsearchcven_US
dc.subjectK-Nearest Neighbors (Knn)en_US
dc.subjectLinear Discriminant Analysis (Lda)en_US
dc.subjectMachine Learning (Ml)en_US
dc.subjectMean Absolute Error (Mae)en_US
dc.subjectRandom Forest (Rf)en_US
dc.subjectSupport Vector Machine (Svm)en_US
dc.subjectBehavioral Researchen_US
dc.subjectDiagnosisen_US
dc.subjectDiscriminant Analysisen_US
dc.subjectForecastingen_US
dc.subjectLearning Algorithmsen_US
dc.subjectLearning Systemsen_US
dc.subjectNearest Neighbor Searchen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectDecision Treeen_US
dc.subjectExtreme Gradient Boosting (Xgboost)en_US
dc.subjectGradient Boostingen_US
dc.subjectGridsearchcven_US
dc.subjectK-Near Neighboren_US
dc.subjectLinear Discriminant Analyseen_US
dc.subjectLinear Discriminant Analyzeen_US
dc.subjectMachine Learningen_US
dc.subjectMachine-Learningen_US
dc.subjectMean Absolute Erroren_US
dc.subjectNearest-Neighbouren_US
dc.subjectRandom Foresten_US
dc.subjectRandom Forestsen_US
dc.subjectSupport Vector Machineen_US
dc.subjectSupport Vectors Machineen_US
dc.subjectDecision Treesen_US
dc.titleSymptom Based Health Status Prediction via Decision Tree, KNN, XGBoost, LDA, SVM, and Random Forest
dc.typeConference Object

Dosyalar