Explainable AI-assisted web application in cancer drug value prediction - Kothari, Sonali; Sharma, Shivanandana; Shejwal, Sanskruti; Kazi, Aqsa; D'Silva, Michela; Karthikeyan, M. (MethodsX, 2024)
<div class="u-margin-s-bottom" id="spara003" style="box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-left: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px; margin-bottom: 16px !important;"> In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes <ul class="list" style="box-sizing: border-box; margin: 16px 0px; padding-right: 0px; padding-left: 0px; list-style: none; display: grid; grid-template-columns: fit-content(15%) fit-content(85%); gap: 0px 16px;"> • An XAI model for cancer drug value prediction. The model provides data that is easy to understand and explain, which is critical for medical decision-making. It also produces accurate projections. • A model outperformed existing models due to extensive training and evaluation on a large cancer medication chemical compounds dataset. • Insights into the causation and correlation between the dependent and independent actors in the chemical composition of the cancer cell. <div class="u-margin-s-bottom" id="spara007" style="box-sizing: border-box; margin-top: 0px; margin-right: 0px; margin-left: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px; margin-bottom: 16px !important;"> While the model is evaluated on Lung Cancer data, the architecture offered in the proposed solution is cancer agnostic. It may be scaled out to other cancer cell data if the properties are similar. The work presents a viable route for customizing treatments and improving patient outcomes in oncology by combining XAI with a large dataset. This research attempts to create a framework where a user can upload a test case and receive forecasts with explanations, all in a portable PDF report.