Disease Diagnosis Using Machine Learning on Electronic Health Records

In healthcare, disease diagnosis is essential because it allows for timely and accurate treatment decisions. Machine learning techniques have emerged as promising tools for disease diagnosis due to the increasing amount of electronic health records (EHRs). This paper presents a comprehensive study on disease diagnosis using machine learning algorithms applied to electronic health records and addresses the need for efficient, accurate, and personalized disease diagnosis and prediction. The research focuses on leveraging patient data, including medical history, laboratory results, and clinical notes, to develop predictive models for accurate disease identification. A diverse dataset of anonymized Electronic Health Records is utilized for experimentation and evaluation. A range of machine learning algorithms, such as random forests, KNN, naïve bayes, decision trees, and support vector machines are implemented and evaluated based on their interpretability, computational efficiency, and diagnostic accuracy. To improve model performance, techniques specific to the dataset for feature selection and data preprocessing are also researched. The research results show that machine learning techniques are more accurate and efficient than conventional diagnostic techniques when it comes to diagnosing diseases.