TITLE:
Estimating the Likelihood of Cancer Occurrence Based on Patient Data and Lifestyle Factors: A Comparison between Logit and Probit Regression
AUTHORS:
Wakeel A. Kasali, Barakat O. Ogunjoun, Abdullahi O. Olakehinde, Emmanuel O. Oderinde, Abibat O. Salam
KEYWORDS:
Logit, Probit, and Lung Cancer
JOURNAL NAME:
Open Access Library Journal,
Vol.11 No.8,
August
29,
2024
ABSTRACT: This study compares logit and probit regression models to analyze the likelihood of lung cancer occurrence based on patient data and lifestyle factors. The results reveal significant associations between lung cancer and patients’ age, gender, smoking status, yellow finger, anxiety, chronic disease, fatigue, allergy, wheezing, alcohol consumption, coughing, shortness of breath, swallowing difficulty, and chest pain. The result shows that the probit model better predicts lung cancer based on the factors observed as its diagnostic performs better than logit. Recommendations based on the findings emphasize the need to create awareness about the significant factors that influence lung cancer. Conclusively, this study contributes to a deeper understanding of the patient’s data and lifestyle factors influencing lung cancer and provides valuable insights.