{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T21:17:12Z","timestamp":1762809432223},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684000","type":"print"},{"value":"9781643684017","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,29]]},"abstract":"<jats:p>This study suggests a novel Acute Lymphoblastic Leukemia (ALL) diagnostic model, built solely on complete blood count (CBC) records. Using a dataset comprised of CBC records of 86 ALL and 86 control patients respectively, we identified the most ALL-specific parameters using a feature selection approach. Next, Grid Search-based hyperparameter tuning with a five-fold cross-validation scheme was adopted to build classifiers using Random Forest, XGBoost, and Decision Tree algorithms. A comparison between the performances of the three models demonstrates that Decision Tree classifier outperformed XGBoost and Random Forest algorithms in ALL detection using CBC-based records.<\/jats:p>","DOI":"10.3233\/shti230479","type":"book-chapter","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:52:40Z","timestamp":1688111560000},"source":"Crossref","is-referenced-by-count":4,"title":["An Artificial Intelligence-Based Diagnostic System for Acute Lymphoblastic Leukemia Detection"],"prefix":"10.3233","author":[{"given":"Yousra","family":"El Alaoui","sequence":"first","affiliation":[{"name":"College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar"}]},{"given":"Regina","family":"Padmanabhan","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar"}]},{"given":"Adel","family":"Elomri","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar"}]},{"given":"Marwa K.","family":"Qaraqe","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar"}]},{"given":"Halima","family":"El Omri","sequence":"additional","affiliation":[{"name":"Medical Oncology-Hematology Department, National Centre for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar"}]},{"given":"Ruba","family":"Yasin Taha","sequence":"additional","affiliation":[{"name":"Medical Oncology-Hematology Department, National Centre for Cancer Care and Research (NCCCR), Hamad Medical Corporation (HMC), Doha, Qatar"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Healthcare Transformation with Informatics and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230479","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:52:41Z","timestamp":1688111561000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"ISBN":["9781643684000","9781643684017"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230479","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,29]]}}}