{"doi":"10.1109/csnt54456.2022.9787574","title":"Liver Disease Prediction using Machine learning Classification Techniques","abstract":null,"journal":"2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT)","year":2022,"id":597982,"datarank":0.7168685239667295,"base_score":4.77912349311153,"endowment":4.77912349311153,"self_citation_contribution":0.7168685239667295,"citation_network_contribution":0.0,"self_endowment_contribution":0.7168685239667295,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":118,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1532084,"name":"Nasmin Jiwani","orcid":null,"position":1,"is_corresponding":false},{"id":1532085,"name":"Neda Afreen","orcid":null,"position":2,"is_corresponding":false},{"id":1532086,"name":"Divyarani D","orcid":null,"position":3,"is_corresponding":false},{"id":1532083,"name":"Ketan Gupta","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Liver Disease Prediction using Machine learning Classification Techniques","abstract":"Machine Learning is a process which is used to discover patterns in huge data/ large data set to enable decision, thereby allowing machines to go through a learning process (i.e. supervised, unsupervised and semi-supervised or reinforced). The data set used in this paper is Liver Patient taken from UCI Repository (i.e. Supervised Learning). There is a plenty of data on patients undergoing medical examination at hospitals and these data has been extracted on liver patients whose information can be further used for future improvement of their conditions. In other words, historical and classified input of patients and output data is fed into various algorithms or classifiers for predicting the future data of patients. The algorithms used here for predicting liver patients are Logistic regression, Decision Tree, Random Forest, KNNeighbor, Gradient Boosting, Extreme Gradient Boosting, LightGB. Based on the analysis and result calculations, it was found that these algorithm has obtained good accuracy after feature selection.","is_dataset_classified":null,"base_score":4.77912349311153,"endowment":4.77912349311153,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"23304386","pmcid":null,"openalex_id":"https://openalex.org/W4281866327","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2022,"count":7},{"year":2023,"count":38},{"year":2024,"count":38},{"year":2025,"count":27},{"year":2026,"count":8}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/9787399/9787559/09787574.pdf?arnumber=9787574","host_type":"publisher"},{"url":"https://doi.org/10.1109/csnt54456.2022.9787574","host_type":"conference"}],"fields_of_study":["Artificial Intelligence in Healthcare","Retinal Imaging and Analysis","Currency Recognition and Detection"],"mesh_terms":[],"keywords":["Machine learning","Gradient boosting","Decision tree","Artificial intelligence","Random forest","Boosting (machine learning)","Computer science","Feature selection","Supervised learning","Logistic regression","Statistical classification","Semi-supervised learning","Data mining","Artificial neural network"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-28T14:44:14.854125Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}