Disease Diagnosis and Management Using Machine Learning and Power BI

The timely identification of diseases is critical for better patient health outcomes and lowerhealthcare expenditures. This research presents an artificial intelligence-driven system designed fordisease diagnosis and management, utilizing machine learning algorithms and the Streamlit framework.The system processes health information provided by users to forecast the likelihood of developingconditions such as diabetes, cardiovascular disease, and Parkinson’s disease. Through an accessible userinterface, individuals input their medical data, which is then analyzed by pre-existing machine learningmodels to produce immediate risk assessments. The use of stored model files ensures rapid and reliabledisease categorization. Furthermore, Power BI is incorporated to improve healthcare oversight throughdynamic data visualization and real-time analytical capabilities.The system assesses crucial health metrics,including blood pressure, cholesterol levels, body mass index (BMI), glucose levels, electrocardiogram(ECG) readings, and vocal biomarkers, to determine disease susceptibility. By integrating machinelearning techniques with sophisticated visualization tools, this approach facilitates early diagnosis,supports proactive health management strategies, and equips both patients and healthcare providers withthe necessary information for well-informed decisions. Future development efforts may concentrate onenhancing the precision of the models, broadening the spectrum of diseases that can be predicted, andincorporating real-time virtual medical consultations to increase accessibility and the quality of patientcare.