A Hybrid Algorithm for Medical Diagnosis
Medical diagnosis and prognosis is an emblematic example for classification problems. Machine learning could provide invaluable support for automatically inferring diagnostic rules from descriptions of past cases, making the diagnosis process more objective and reliable. Since the problem involves b...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 673 |
|---|---|
| container_issue | |
| container_start_page | 668 |
| container_title | |
| container_volume | |
| creator | Bratu, C.V. Savin, C. Potolea, R. |
| description | Medical diagnosis and prognosis is an emblematic example for classification problems. Machine learning could provide invaluable support for automatically inferring diagnostic rules from descriptions of past cases, making the diagnosis process more objective and reliable. Since the problem involves both test and misclassification costs, we have analyzed ICET, the most prominent approach in the literature for complex cost problems. The hybrid algorithm tries to avoid the pitfalls of traditional greedy induction by performing a heuristic search in the space of possible decision trees through evolutionary mechanisms. Our implementation solves some of the problems of the initial ICET algorithm, proving it to be a viable solution for the problem considered. |
| doi_str_mv | 10.1109/EURCON.2007.4400571 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4400571</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4400571</ieee_id><sourcerecordid>4400571</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-1d54b46d986c34564deefca0c07408d9b289e5bafc3adae7b64ee32271a3c48d3</originalsourceid><addsrcrecordid>eNo1j81KAzEYRSMiqHWeoJss3cz45WcmyXIYayvUFkoFdyU_39TI1JGkm769BevqcDaHewmZMqgYA_M0e99061XFAVQlJUCt2BW5Z5KfRTPxcU0Ko_S_c35Lipy_AICpRhij7shjSxcnl2Kg7bAfUzx-Hmg_JvqGIXo70Odo999jjvmB3PR2yFhcOCHbl9m2W5TL9fy1a5dlNHAsWailk00wuvFC1o0MiL234EGdFwTjuDZYO9t7YYNF5RqJKDhXzAovdRATMv3LRkTc_aR4sOm0u3wTvxCtQe8</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A Hybrid Algorithm for Medical Diagnosis</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Bratu, C.V. ; Savin, C. ; Potolea, R.</creator><creatorcontrib>Bratu, C.V. ; Savin, C. ; Potolea, R.</creatorcontrib><description>Medical diagnosis and prognosis is an emblematic example for classification problems. Machine learning could provide invaluable support for automatically inferring diagnostic rules from descriptions of past cases, making the diagnosis process more objective and reliable. Since the problem involves both test and misclassification costs, we have analyzed ICET, the most prominent approach in the literature for complex cost problems. The hybrid algorithm tries to avoid the pitfalls of traditional greedy induction by performing a heuristic search in the space of possible decision trees through evolutionary mechanisms. Our implementation solves some of the problems of the initial ICET algorithm, proving it to be a viable solution for the problem considered.</description><identifier>ISBN: 9781424408122</identifier><identifier>ISBN: 1424408121</identifier><identifier>EISBN: 142440813X</identifier><identifier>EISBN: 9781424408139</identifier><identifier>DOI: 10.1109/EURCON.2007.4400571</identifier><language>eng</language><publisher>IEEE</publisher><subject>Computer science ; cost-sensitive learning ; Costs ; Decision trees ; hybrid algorithm ; Inference algorithms ; Machine learning ; Machine learning algorithms ; Medical diagnosis ; Medical treatment ; Physics computing ; Testing</subject><ispartof>EUROCON 2007 - The International Conference on "Computer as a Tool", 2007, p.668-673</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4400571$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,2052,27906,54901</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4400571$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Bratu, C.V.</creatorcontrib><creatorcontrib>Savin, C.</creatorcontrib><creatorcontrib>Potolea, R.</creatorcontrib><title>A Hybrid Algorithm for Medical Diagnosis</title><title>EUROCON 2007 - The International Conference on "Computer as a Tool"</title><addtitle>EURCON</addtitle><description>Medical diagnosis and prognosis is an emblematic example for classification problems. Machine learning could provide invaluable support for automatically inferring diagnostic rules from descriptions of past cases, making the diagnosis process more objective and reliable. Since the problem involves both test and misclassification costs, we have analyzed ICET, the most prominent approach in the literature for complex cost problems. The hybrid algorithm tries to avoid the pitfalls of traditional greedy induction by performing a heuristic search in the space of possible decision trees through evolutionary mechanisms. Our implementation solves some of the problems of the initial ICET algorithm, proving it to be a viable solution for the problem considered.</description><subject>Computer science</subject><subject>cost-sensitive learning</subject><subject>Costs</subject><subject>Decision trees</subject><subject>hybrid algorithm</subject><subject>Inference algorithms</subject><subject>Machine learning</subject><subject>Machine learning algorithms</subject><subject>Medical diagnosis</subject><subject>Medical treatment</subject><subject>Physics computing</subject><subject>Testing</subject><isbn>9781424408122</isbn><isbn>1424408121</isbn><isbn>142440813X</isbn><isbn>9781424408139</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j81KAzEYRSMiqHWeoJss3cz45WcmyXIYayvUFkoFdyU_39TI1JGkm769BevqcDaHewmZMqgYA_M0e99061XFAVQlJUCt2BW5Z5KfRTPxcU0Ko_S_c35Lipy_AICpRhij7shjSxcnl2Kg7bAfUzx-Hmg_JvqGIXo70Odo999jjvmB3PR2yFhcOCHbl9m2W5TL9fy1a5dlNHAsWailk00wuvFC1o0MiL234EGdFwTjuDZYO9t7YYNF5RqJKDhXzAovdRATMv3LRkTc_aR4sOm0u3wTvxCtQe8</recordid><startdate>200709</startdate><enddate>200709</enddate><creator>Bratu, C.V.</creator><creator>Savin, C.</creator><creator>Potolea, R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200709</creationdate><title>A Hybrid Algorithm for Medical Diagnosis</title><author>Bratu, C.V. ; Savin, C. ; Potolea, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-1d54b46d986c34564deefca0c07408d9b289e5bafc3adae7b64ee32271a3c48d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Computer science</topic><topic>cost-sensitive learning</topic><topic>Costs</topic><topic>Decision trees</topic><topic>hybrid algorithm</topic><topic>Inference algorithms</topic><topic>Machine learning</topic><topic>Machine learning algorithms</topic><topic>Medical diagnosis</topic><topic>Medical treatment</topic><topic>Physics computing</topic><topic>Testing</topic><toplevel>online_resources</toplevel><creatorcontrib>Bratu, C.V.</creatorcontrib><creatorcontrib>Savin, C.</creatorcontrib><creatorcontrib>Potolea, R.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bratu, C.V.</au><au>Savin, C.</au><au>Potolea, R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Hybrid Algorithm for Medical Diagnosis</atitle><btitle>EUROCON 2007 - The International Conference on "Computer as a Tool"</btitle><stitle>EURCON</stitle><date>2007-09</date><risdate>2007</risdate><spage>668</spage><epage>673</epage><pages>668-673</pages><isbn>9781424408122</isbn><isbn>1424408121</isbn><eisbn>142440813X</eisbn><eisbn>9781424408139</eisbn><abstract>Medical diagnosis and prognosis is an emblematic example for classification problems. Machine learning could provide invaluable support for automatically inferring diagnostic rules from descriptions of past cases, making the diagnosis process more objective and reliable. Since the problem involves both test and misclassification costs, we have analyzed ICET, the most prominent approach in the literature for complex cost problems. The hybrid algorithm tries to avoid the pitfalls of traditional greedy induction by performing a heuristic search in the space of possible decision trees through evolutionary mechanisms. Our implementation solves some of the problems of the initial ICET algorithm, proving it to be a viable solution for the problem considered.</abstract><pub>IEEE</pub><doi>10.1109/EURCON.2007.4400571</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9781424408122 |
| ispartof | EUROCON 2007 - The International Conference on "Computer as a Tool", 2007, p.668-673 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_4400571 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computer science cost-sensitive learning Costs Decision trees hybrid algorithm Inference algorithms Machine learning Machine learning algorithms Medical diagnosis Medical treatment Physics computing Testing |
| title | A Hybrid Algorithm for Medical Diagnosis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T05%3A36%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20Hybrid%20Algorithm%20for%20Medical%20Diagnosis&rft.btitle=EUROCON%202007%20-%20The%20International%20Conference%20on%20%22Computer%20as%20a%20Tool%22&rft.au=Bratu,%20C.V.&rft.date=2007-09&rft.spage=668&rft.epage=673&rft.pages=668-673&rft.isbn=9781424408122&rft.isbn_list=1424408121&rft_id=info:doi/10.1109/EURCON.2007.4400571&rft_dat=%3Cieee_6IE%3E4400571%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=142440813X&rft.eisbn_list=9781424408139&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4400571&rfr_iscdi=true |