Survival Analysis of Oncological Patients Using Machine Learning Method

Abstract

Currently, a considerable volume of information is collected and stored by large health institutions. These data come from medical records and hospital records, and the Hospital Cancer Registry is a database for integrating data from hospitals throughout Iraq. The data mining (DM) technique provides knowledge previously not visible in the database and can be used to predict trends or describe characteristics of the past. DM methods can include classification, generalisation, characterisation, clustering, association, evolution, pattern discovery, data visualisation, and rule-guided mining techniques to perform survival analyses that take into account all the patient’s medical record variables. For four of the eleven groups examined, this accuracy was relatively high. The database of patients treated by the Baghdad Teaching Hospital between 2018 and 2021 was examined using a classification of the most crucial variables for event prediction, and a distinctive pattern was found. Machine learning techniques allow a global assessment of the data that is available and produce results that can be interpreted as significant information for epidemiological studies, even in cases where the sample is small and there is a lack of information on several variables.

Development and validation of machine-learning-based survival prediction model for young patients with gastric cancer

“…With the development of ML, remarkable results have been achieved by applying it for risk prediction and survival analysis [33,34]. Speci cally, survival ML models can handle risk changes and model complex relationships between variables and outcomes, making them more exible and accurate for predicting survival across various clinical settings [35].…”

User Perceptions of ROTEM-Guided Haemostatic Resuscitation: A Mixed Qualitative–Quantitative Study

“…Its applications found their way into clinical routine, significantly improving patient care [ 21 , 23 ]. Multiple studies have explored the potential use of artificial intelligence in many areas of medicine, such as cardiology [ 21 ], neurology [ 20 ], oncology [ 22 ], haematology [ 42 ], nephrology [ 43 ], gastroenterology, hepatology, orthopaedics and rheumatology [ 21 ]. The findings hold great promise for revolutionising clinical care, not only in terms of better diagnostic and therapeutic options for patients but also by facilitating decision-making and reducing cognitive load for clinicians.…”

“…It is, therefore, necessary to consider how clinicians working with this technology can be supported. One of the solutions for significantly improving patient care is the application of artificial intelligence and machine learning [ 20 , 21 , 22 , 23 ]. Situation awareness and user-centred design principles also play an essential role in facilitating decision-making in complex clinical situations [ 24 , 25 ].…”

Support Vector Machine Approaches for Tumor Classification and Survival Prediction in Cancer Patients: A Multi-Omics Data Analysis

“…Our consideration includes four unmistakable SVM calculations: Linear SVM, Polynomial SVM, Radial Basis Function (RBF) SVM, and Sigmoid SVM. The tests were conducted on a comprehensive dataset comprising multi-omics profiles from different cancer sorts, counting breast, lung, prostate, and colorectal cancer [9]. Our dataset comprises atomic profiles gotten from high-throughput omics innovations, counting genomics, transcriptomics, proteomics, and metabolomics.…”