Risk Factor Analysis and Precision Treatment Enhancement through Comparative Data-Driven Algorithms for Clinical Records

Healthcare analytics is a new field that uses data analysis, machine learning, and clinical expertise to improve patient outcomes and make healthcare services more efficient. The quick digitisation of medical records and the widespread use of Electronic Health Records (EHRs) in recent years have led to an unprecedented amount of clinical and demographic data. This large amount of data makes it possible to go beyond standard medical practices by using computer models to predict diseases, assess risks, and plan individualised treatments. Healthcare analytics combines machine learning algorithms with clinical knowledge to find useful information in large datasets. This can reveal patterns and relationships that might not be clear from regular clinical observation. As patient care becomes more complicated and chronic and lifestyle-related diseases become more common, it is more important than ever for healthcare professionals to have predictive models that can help them make quick and smart decisions. Traditional healthcare methods often use manual assessments and separate diagnostic tests, which can take a long time, be subjective, and make mistakes. Machine learning models trained on EHR data, on the other hand, can make automated, accurate, and scalable predictions that help doctors find high-risk patients, predict how a disease will progress, and come up with personalized intervention strategies. This change toward making decisions based on data not only makes healthcare delivery more efficient, but it also makes precision medicine possible. In precision medicine, treatments and preventive measures are tailored to each patient's profile, taking into account things like demographics, clinical history, lab results, and comorbidities. The main goal of this study is to create and test several machine learning models that can predict important health outcomes, with a focus on cancer, diabetes, diabetic retinopathy, and heart disease. These conditions are chosen because they are common, cause a lot of illness and death, and can be treated early. The study employs a comparative methodology utilising demographic and clinical data sourced from Electronic Health Records (EHRs) to assess the efficacy of Support Vector Machines (SVM), Decision Trees (DT), Logistic Regression (LR), and Random Forests (RF). The study's goal is to find out which algorithms give the best accuracy, precision, and recall for each disease by testing these models on a variety of datasets. This will help doctors choose the most reliable predictive tools for use in the real world. The results of the experiments show that the performance is good across all of the chosen diseases. Support Vector Machines and Decision Trees were very good at predicting cancer (97.08%) and diabetes (97.33%), respectively. Logistic Regression showed a high accuracy of 76.52% for diabetic retinopathy, showing that it works well with structured datasets where linear relationships exist. Decision Trees were the best at predicting heart disease, with an accuracy rate of 86.41%. SVM used on the Pima diabetes dataset had an accuracy rate of 79.746%. These results show how important it is to compare different models, since no one algorithm works better than others for all types of diseases. By looking at different models, healthcare professionals can find the best way to handle each clinical situation. This makes predictions more reliable and supports interventions that are based on evidence. The research focuses on how these models can be used in real life in healthcare settings, not just on how well they work with numbers. The system is meant to work with clinical workflows so that doctors can see risk assessments, see patient trends, and get useful advice. Also, using strong evaluation metrics like accuracy, precision, and recall makes sure that predictions are not only statistically sound but also useful in the real world. This research enhances patient care, facilitates early diagnosis, and promotes preventive healthcare strategies by integrating computational modelling with clinical decision-making. In conclusion, this study marks a substantial advancement in healthcare analytics by illustrating the capacity of machine learning to revolutionise disease prediction and personalised medicine. The study shows how to use EHR data and compare different predictive models to make it easier to use advanced analytics in everyday clinical practice. The high accuracy achieved for critical diseases shows that AI-driven solutions can improve patient outcomes, lower healthcare costs, and help doctors make quick, well-informed decisions. The proposed system is the basis for future progress in precision medicine. It provides scalable, reliable, and easy-to-understand predictive tools that can change as healthcare needs change.