Machine Learning Approaches For Early Detection Of Multiple Diseases Using Healthcare Data

Abstract

This paper aims at exploring the application of machine learning algorithms for early disease diagnosis through health information. A comprehensive dataset from Kaggle was used, involving 500 patient records with 23 variables, including symptoms of common diseases like as diabetes, malaria, dengue, typhoid, and hepatitis B. For improved data integrity and relevance, the dataset underwent significant preprocessing, which included missing value removal and feature selection. Five machine learning techniques, including GBC, GNB, RF, LR, and SVC, were tested for their ability to predict disease start. The results showed great accuracy rates across many methods, with RF having the best accuracy of 99.00%. Feature significance analysis offered information on the most selective symptoms for illness prediction. Furthermore, the study showed the social benefits of early disease identification, stressing better patient outcomes and lower healthcare expenses. Ethical considerations, sustainability strategies, and future research opportunities were discussed to help guide the appropriate deployment and continual improvement of predictive models in healthcare settings. This study improves the field of early detection of illness and highlights the transformational potential of machine learning in healthcare delivery. Future research topics include hybrid techniques, algorithmic bias protection, and adding predictive models into clinical decision support systems to improve diagnostic accuracy and patient care.