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
- healthcare
- public health
- end stage renal disease
- deep learning
- mental health
- squamous cell carcinoma
- cardiovascular events
- newly diagnosed
- small cell lung cancer
- clinical practice
- risk factors
- chronic kidney disease
- ejection fraction
- clinical trial
- artificial intelligence
- type diabetes
- risk assessment
- quality improvement
- patient reported outcomes
- cardiovascular disease
- early stage
- radiation therapy
- electronic health record
- climate change
- neoadjuvant chemotherapy
- social media
- combination therapy
- rectal cancer
- double blind
- cross sectional
- data analysis
- health information
- human health
- sentinel lymph node