An Early Prediction of Diseases via M-L Based Algorithm in Hybrid with the Existing Structure

• Published in: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) pp. 1295 - 1299
• Main Authors: Habelalmateen, Mohammed I., Singh, Rajesh, Chowdhury, Sohini, Sanaboina, Svsv Prasad, Badhoutiya, Arti, Kumar, Neelam Sanjeev
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.05.2024
• Subjects: Chronic diseases; Convolutional neural networks; Data mining; Disease prevalence; Early detection; Feature extraction; Machine learning; Machine learning algorithms; Medical services; Prediction; Prediction algorithms
• DOI: 10.1109/ICACITE60783.2024.10616638

Due to alterations in the environment and changes in lifestyle, illnesses are becoming more common in the modern day. Many illnesses cannot advance to a more dangerous stage without early discovery and prognosis. However, while personally assessing patients, doctors are prone to errors or discrepancies. Thus, developing a trustworthy technique to identify and forecast the incidence of chronic illnesses is the primary goal of this research. To achieve this purpose, we employ cutting-edge machine learning, and we promise precise illness categorization. Regarding the prediction of illness, data mining is crucial. Our suggested method makes use of machine learning techniques like Convolutional Neural Network (CNN) for automated feature extraction and matching to allow precise illness prediction. Our technology provides a wide range of illness prognoses depending on the symptoms of specific patients by analysing this dataset.