Revolutionizing Healthcare: Disease Prediction Through Machine Learning Algorithms

Author: Andia Vllamasi, Klea Hallaçi
Affiliation: POLIS University

A crucial field of medical research is disease prediction, which has the potential to improve early diagnosis and therapy that can have a major impact on the course of treatment. By dramatically raising the standard of patient care and the effectiveness of the healthcare system as a whole, disease prediction plays a critical role in contemporary healthcare. Early detection of illnesses or medical issues, even before symptoms appear, is a key component of this proactive approach to healthcare management. This enables prompt interventions, better treatment outcomes, and better resource allocation. In this study, we use four different machine learning techniques to predict diseases using large datasets. Our main goal is to evaluate the effectiveness of different algorithms and determine which one performs best at accurately predicting the condition. To guarantee data quality and significance, the study makes considerable use of feature selection, engineering, and data preparation. Across various illness datasets, four machine learning algorithms, K-Nearest Neighbors, XG Boost, Ada Boost with SVM and Logistic Regression, are thoroughly examined. Accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve (AUC-ROC) are just a few of the performance criteria used to rate these algorithms. The comparative study not only identifies the algorithm with the best predicted accuracy, but it also offers insightful information about the benefits and drawbacks of each strategy. This study has significant healthcare impacts. We provide medical professionals with an effective tool for early detection and intervention by determining the algorithm that performs best at disease prediction. Improved disease prediction accuracy can result in earlier and more efficient treatment, which may save lives and lower healthcare costs. Additionally, this research opens the door for the application of sophisticated machine learning methods to clinical practice, ushering in a new era in healthcare where data-driven predictions support clinical judgment. In conclusion, by utilizing the potential of machine learning algorithms for more precise and timely disease prediction, our research supports the continual evolution of healthcare.