Analysis of Cancer Category using Bidirectional LSTM from Medical Records

Analyzing medical data effectively is critical for making educated decisions and providing patient care in this age of quickly improving healthcare technology. Natural language processing methods, particularly text classification and sentiment analysis in cancer patients’ medical records is the primary objective of the proposed work. The goal is to help healthcare professionals diagnose and treat patients more effectively by extracting meaningful insights from textual data. Logistic Regression is used as separate text analysis method in the research. In order to get a full picture of patients’ histories, these models are trained and tested to see how well they categorize medical information pertaining to cancer into certain predetermined groups. To make sure the input data is good, data preparation is essential. Remove stop words, special letters, and any unnecessary information from the text to make it cleaner. In addition, the dataset is examined to learn about its properties and distribution which understanding of the data that lie underneath. The suggested models are successful, according to the experimental data; the Bidirectional LSTM model had Healthcare practitioners must pay close attention to these results because they pave the way for improved decision support via the accurate and rapid identification of pertinent medical information. This study highlights the value of natural language processing methods in analyzing cancer patients’ medical records, which is a contribution to healthcare analytics as a whole. In addition, it highlights how Bidirectional LSTM models may improve medical record classification, which in turn improves patient care and healthcare decision-making.