Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model

Healthcare and medicine are key areas where machine learning algorithms are widely used. The medical decision support systems thus created are accurate enough, however, they suffer from the lack of transparency in decision making and shows a black box behavior. However, transparency and trust are significant in the field of health and medicine and hence, a black box system is sub optimal in terms of widespread applicability and reach. Hence, the explainablility of the research make the system reliable and understandable, thereby enhancing its social acceptability. The presented work explores a thyroid disease diagnosis system. SHAP, a popular method based on coalition game theory is used for interpretability of results. The work explains the system behavior both locally and globally and shows how machine leaning can be used to ascertain the causality of the disease and support doctors to suggest the most effective treatment of the disease. The work not only demonstrates the results of machine learning algorithms but also explains related feature importance and model insights.

Component-based Analysis of Dynamic Search Performance

In many search scenarios, such as exploratory, comparative, or survey-oriented search, users interact with dynamic search systems to satisfy multi-aspect information needs. These systems utilize different dynamic approaches that exploit various user feedback granularity types. Although studies have provided insights about the role of many components of these systems, they used black-box and isolated experimental setups. Therefore, the effects of these components or their interactions are still not well understood. We address this by following a methodology based on Analysis of Variance (ANOVA). We built a Grid Of Points that consists of systems based on different ways to instantiate three components: initial rankers, dynamic rerankers, and user feedback granularity. Using evaluation scores based on the TREC Dynamic Domain collections, we built several ANOVA models to estimate the effects. We found that (i) although all components significantly affect search effectiveness, the initial ranker has the largest effective size, (ii) the effect sizes of these components vary based on the length of the search session and the used effectiveness metric, and (iii) initial rankers and dynamic rerankers have more prominent effects than user feedback granularity. To improve effectiveness, we recommend improving the quality of initial rankers and dynamic rerankers. This does not require eliciting detailed user feedback, which might be expensive or invasive.

IMPLEMENTASI METODE AHP PADA SISTEM PENDUKUNG KEPUTUSAN PARIWISATA JAWA TIMUR

Perubahan perilaku wisatawan dalam berwisata dengan bantuan perangkat digital salah satunya adalah perencanaan wisata, contohnya yaitu menentukan destinasi wisata. Pemilihan destinasi wisata yang bervariasi di Provinsi Jawa Timur oleh para calon wisatawan dapat dipermudah guna meningkatkan strategi manajemen pariwisata. Penelitian ini bertujuan untuk menghasilkan Sistem Pendukung Keputusan atau Decision Support System (DSS) berbasis website yang dapat mempermudah wisatawan dalam menentukan pilihan destinasi pariwisata di wilayah Jawa Timur. Sebagai metode pengambilan keputusan, Analytical Hierarchy Process (AHP) diterapkan. Metode System Development Life Cycle (SDLC) yang meliputi tahap perencanaan, analisis, desain, dan produksi serta pengujian dan implementasi, digunakan dalam mengembangkan Sistem Pendukung Keputusan. Pengujian khusus sistem dan user-testing menunjukkan bahwa seluruh fungsionalitas yang diuji bekerja dengan baik dengan black box testing, sedangkan user-testing menunjukkan bahwa 87,13% pengguna setuju bahwa sistem ramah pengguna.