Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in the development of disease prediction systems that enhance early diagnosis, clinical decision-making, and patient care. This study reviews the integration of AI techniques—including machine learning (ML) and deep learning (DL) models—in predictive analytics for various diseases such as cardiovascular disorders, diabetes, cancer, and neurological conditions. By analysing large volumes of clinical data from electronic health records (EHRs), medical imaging, and real-time monitoring devices, AI-driven systems can identify complex patterns and risk factors that traditional methods may overlook. Predictive algorithms like Random Forest, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and long short-term memory (LSTM) networks demonstrate high accuracy in forecasting disease onset and progression, often surpassing conventional statistical models. Despite significant promise, challenges such as data heterogeneity, model interpretability, and ethical considerations must be addressed to ensure reliable and equitable deployment in clinical settings. Future research should focus on enhancing model transparency. Artificial Intelligence (AI) is playing a major role in improving the healthcare sector by enabling early disease detection and accurate prediction. This study focuses on AI-based disease prediction systems that use machine learning and deep learning algorithms to analyze medical data such as patient history, symptoms, lab test results, and medical images. AI models like Decision Tree, Random Forest, Support Vector Machine (SVM), Neural Networks, and Convolutional Neural Networks (CNN) help in predicting diseases such as diabetes, heart disease, cancer, and kidney disorders with high accuracy. These systems support doctors in making faster and better decisions, reducing human errors, and improving patient treatment outcomes. However, challenges like data privacy, lack of quality datasets, and model interpretability still exist. The study concludes that AI-driven disease prediction systems have great potential to enhance healthcare services, and future advancements can make them more reliable, secure, and widely usable in real-time clinical environments. The rapid advancement of Artificial Intelligence (AI) has significantly influenced various sectors, particularly healthcare. AI technologies are increasingly being used to develop intelligent systems capable of predicting diseases at an early stage. This study explores the application of artificial intelligence in healthcare with a focus on disease prediction systems. By utilizing machine learning algorithms and large healthcare datasets, AI systems can analyze medical records, symptoms, and historical patient data to detect patterns that indicate the possible occurrence of diseases. Such systems assist doctors in making more accurate and timely decisions, ultimately improving patient outcomes. The research examines the working mechanisms of AI-based prediction models and highlights their advantages in enhancing diagnostic accuracy, reducing healthcare costs, and supporting preventive medicine. Additionally, the study discusses challenges such as data privacy, model reliability, and integration with existing healthcare infrastructure. Overall, AI-driven disease prediction systems represent a promising approach to improving the efficiency and effectiveness of modern healthcare services.
Artificial Intelligence (AI), Healthcare Analytics, Disease Prediction System, Machine Learning, Deep Learning, Predictive Modeling, Clinical Decision Support Systems (CDSS), Big Data in Healthcare, Medical Data Mining, Neural Networks, Supervised Learning Algorithms, Early Disease Detection, Medical Diagnosis Systems, Health Informatics, Data Classification, Risk Assessment Models, Electronic Health Records (EHR), Precision Medicine, Healthcare Automation, AI-based Diagnostic Tools
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