Emerging Patterns in Machine Learning for Disease Detection

Abstract

The growing number of diseases and disorders worldwide further supports the need for prompt and precise diagnosis and classification. Understanding how disease diagnosis can be enhanced through the use of reliable computational techniques including pre process ing approaches, feature selection methods, and hyper parameter tuning with Grid Search Cross-Validation (Grid Search CV), primarily boosts and optimizes feature selection from the machine learning field is the goal of this thesis.” These procedures are essential for ad dressing problems with the developed models’ performance, interpretability, and depend ability. The dataset in the context of disease classification is a comprehensive collection of mea sures. Missing values and encoding have been addressed at the pre processing stage of the data handling process to guarantee a stable and clean baseline for the subsequent steps. Feature selection techniques are employed in order to eliminate insignificant instances and prevent the issue of their dominance. Decision Tree, Random Forest, Support Vector Classifier (SVC), XG Boost, Light GBM and CAT Boost are the six machine learning models that were applied to the dataset. The Grid Search Cross Validation process, which enables the proper configuration of hyper pa rameters, is used to tune the parameters of each model. The evaluation metrics such as confusion matrix, accuracy, precision, recall and F1 score are used to assess models’ per formance. These findings demonstrate how feature selection and hyperparameter modification may be used to enhance the model. This is an interesting finding that raises the possibility of using machine learning to increase early diagnosis rates and decrease diagnostic errors. This study advances the field of machine learning in healthcare and offers a method for effectively and efficiently detecting illnesses.