Diagnosis of Medical Conditions via the Utilization of Machine Learning Techniques on Electronic Health Records

Diagnosing diseases is paramount in healthcare facilitating prompt and precise treatment decisions. The emergence of electronic health records (EHRs) has rendered machine learning methodologies progressively attractive for disease diagnosis. This paper presents a comprehensive analysis of disease diagnosis using electronic health records and machine learning algorithms. It underscores the significance of accurate, tailored, and efficient disease diagnosis and prediction. The research underscores the significance of utilizing patient data, including medical history, laboratory results, and clinical observations, to develop predictive models for precise disease identification. The research employs a diverse collection of anonymized Electronic Health Records for experimentation and evaluation. The study encompasses implementing and evaluating various machine learning algorithms, such as Random Forest, KNN, Naïve Bayes, Decision Tree, and SVM. The assessment hinges on their clarity of interpretation, computational efficiency, and precision in diagnostics. The research further explores techniques tailored to specific datasets for feature selection and data preprocessing, aiming to improve model performance. The results demonstrate that machine learning methodologies surpass traditional diagnostic approaches regarding precision and effectiveness in disease identification.