Human Disease Prediction using Machine Learning Techniques and Real-life Parameters

Abstract

Disease prediction of a human means predicting the probability of a patient's disease after examining the combinations of the patient's symptoms. Monitoring a patient's condition and health information at the initial examination can help doctors to treat a patient's condition effectively. This analysis in the medical industry would lead to a streamlined and expedited treatment of patients. The previous researchers have primarily emphasized machine learning models mainly Support Vector Machine (SVM), K-nearest neighbors (KNN), and RUSboost for the detection of diseases with the symptoms as parameters. However, the data used by the prior researchers for training the model is not transformed and the model is completely dependent on the symptoms, while their accuracy is poor. Nevertheless, there is a need to design a modified model for better accuracy and early prediction of human disease. The proposed model has improved the efficacy and accuracy model, by resolving the issue of the earlier researcher's models. The proposed model is using the medical dataset from Kaggle and transforms the data by assigning the weights based on their rarity. This dataset is then trained using a combination of machine learning algorithms: Random Forest, Long Short-Term Memory (LSTM), and SVM. Parallel to this, the history of the patient can be analyzed using LSTM Algorithm. SVM is then used to conclude, the possible disease. The proposed model has achieved better accuracy and reliability as compared to state-of-the-art methods. The proposed model is useful to contribute towards development in the automation of the healthcare industries.

Hdrp: Integrated Strategies for Robust Heart Disease Detection Using Random Crop Lstm Processing and Regression Models

“…Contrary to methodologies that overlook patient medical history as input data, our approach emphasizes comprehensive patient information, ensuring more effective and human-centered models. This approach enhances model accuracy, particularly in cases where symptoms are location-dependent, acknowledging that certain symptoms may be specific to particular locations [3].…”