Alyasin, Eman IbrahimAta, Oğuz2024-10-242024-10-242024Alyasin, E. I., Ata, O. (2024). Enhancing the diagnosis of liver disease : combining machine learning with the Indian liver patient dataset. Lecture Notes in Networks and Systems / 5th Doctoral Symposium on Computational Intelligence, DoSCI 2024, 1086 LNNS, 225-234. 10.1007/978-981-97-6036-7_199789819760350https://hdl.handle.net/20.500.12939/4947Volume editors : Swaroop A., Kansal V., Fortino G., Hassanien A.E.Thus, this study illustrates a comprehensive examination of machine learning techniques for liver disease diagnosis using the Indian Liver Disease Patients Dataset (ILPD). In view of the critical need to identify liver disorders early and accurately, we used a multimodal machine learning approach involving feature selection, advanced preprocessing, and classifier integration. The use of stacking classifier with ExtraTrees at the meta level, and RF (Random Forest), XGBoost, DT (Decision Tree) and ExtraTrees at the base level is a novelty in our method. When combined with tenfold cross-validation, this technique facilitates extensive evaluation across various data partitions. In contrast to other works that have concentrated on minimizing data imbalances and increasing feature relevance to enhance model prediction accuracies; our work stands out as unique. There was an impressive improvement in accuracy precision and reliability as compared to previous models by our stacking classifier which achieved over 90% accuracy and an AUC score. This demonstration shows why it is necessary to combine several machine learning methods including their application within medical institutions. Also, our study compares itself with the latest researches on similar issues so as to show what has been done differently in our work.eninfo:eu-repo/semantics/closedAccessDimensionality reductionIndian liver patient datasetRandom forestStacking classifierConference Object1086 LNNS2252342-s2.0-85206488231N/A