Disease Prediction using Machine Learning is the system that is used to predict the diseases from the symptoms which are given by the patients or any user. The system processes the symptoms provided by the user as input and gives the output as the probability of the disease. Naïve Bayes classifier is used in the prediction of the disease which is a supervised machine learning algorithm. The probability of the disease is calculated by the Naïve Bayes algorithm. With an increase in biomedical and healthcare data, accurate analysis of medical data benefits early disease detection and patient care. By using linear regression and decision tree we are predicting diseases like Diabetes, Malaria, Jaundice, Dengue, and Tuberculosis.With the rapid growth of artificial intelligence, machine learning (ML) has become a game-changer in healthcare, providing innovative ways to predict diseases and diagnose them early. Traditional diagnostic methods often depend on manual assessments, which can be slow, prone to errors, and reliant on expert interpretation. By integrating ML techniques into disease prediction systems, we can significantly boost accuracy, efficiency, and accessibility in medical diagnostics.This study introduces a machine learning-based disease prediction system that utilizes patient medical data— like symptoms, demographic details, and clinical history—to estimate the likelihood of various diseases. We conduct a comparative analysis of several ML algorithms, including Decision Trees, Random Forest, Support Vector Machines (SVM), and Deep Learning models, to find the most effective approach. The dataset is refined through preprocessing techniques such as feature selection, data normalization, and addressing missing values to enhance model performance. We evaluate the models based on accuracy, precision, recall, and F1-score to ensure reliable predictions.The experimental results show that ML-based disease prediction surpasses traditional diagnostic methods, providing better accuracy and quicker processing times. These findings highlight that machine learning can be vital in early disease detection, ultimately lowering mortality rates and improving patient care.This research emphasizes the increasing significance of artificial intelligence in the healthcare field. Future efforts will aim to expand the dataset, incorporate deep learning architectures, and integrate real-time patient monitoring systems to further enhance disease prediction capabilities. By advancing ML-based diagnostic tools, this study contributes to the creation of intelligent healthcare solutions that improve early detection, treatment planning, and overall patient outcomes.
Disease Prediction, Machine learning.
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