Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study.
Gohar MohammadiMehdi Azizmohammad LoohaMohammad Amin PourhoseingholiMostafa Rezaei TaviraniSamaneh SohrabiAmirali Zareie Shab KhanehHassan PiriMaryam AlaeiNaser ParvaniIman VakilzadehSara JavadiZeynab Moradian Haft CheshmehZahra RazzaghiReza Mahmoud RobatiMona Zamanian AzodiSaba Zarean ShahrakiMelika HadaviRaheleh TalebiJamshid Charati YazdaniMohammad Esmaeil MotlaghSoheila KhodakarimPublished in: Asian Pacific journal of cancer prevention : APJCP (2024)
This study highlights the significance of variables including time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type in predicting CRC survival. The NB model exhibited optimal efficacy in mortality prediction, maintaining a balanced sensitivity and specificity. Policy recommendations encompass early diagnosis and treatment initiation for CRC patients, improved data collection through digital health records and standardized protocols, support for predictive analytics integration in clinical decisions, and the inclusion of identified prognostic variables in treatment guidelines to enhance patient outcomes.
Keyphrases
- machine learning
- lymph node
- big data
- public health
- healthcare
- end stage renal disease
- deep learning
- chronic kidney disease
- small cell lung cancer
- artificial intelligence
- newly diagnosed
- cardiovascular events
- risk factors
- ejection fraction
- clinical practice
- cardiovascular disease
- coronary artery disease
- risk assessment
- prognostic factors
- early stage
- climate change
- patient reported outcomes