Machine Learning-Based Diagnosis and Detection of Liver Cancer: An Approach Enhancement

One of the most important organs in our body is the liver, yet liver cancer incidence has been sharply rising in recent years, primarily as a result of poor lifestyles. In order to accurately identify the ailment, this study suggests a diagnosis system based on multiple machine-learning techniques. After managing the dataset's missing values, data encoding, and segregation are carried out for pre-processing. Upon conducting feature scaling on the used dataset, machine learning models are applied. Out of the seven models utilized, the findings demonstrate that SVM and LGB classifiers perform exceptionally well in terms of the highest accuracy and least amount of loss, while the Random Forest approach surpassed all others when recall, F1 score, and precision are to be considered.