Medical Disease Prediction Research Articles

Smart Voice Based Medical Chatbot for Disease Prediction

The global shortage of medical resources has intensified in recent years, exacerbated by a growing and aging population, particularly following the pandemic. However, advancements in robotics and artificial intelligence have empowered healthcare professionals to address these challenges. Intelligent Speech Technology (IST) is one such innovation that has helped doctors and patients enhance medical efficiency and reduce cumbersome tasks. Despite this, complex hospital environments with background noise and variations in patient pronunciation have limited IST's broader application. Yet, as machine learning technologies rapidly advance intelligent speech recognition capabilities, these obstacles are expected to be overcome in the near future. This project focuses on the concept of an AI-based voice- assisted chatbot for medical disease prediction. Deployed on a Raspberry Pi as a standalone hardware device, it utilizes natural language processing (NLP) and machine learning (ML) techniques to detect diseases by interacting with users through a series of questions. A groundbreaking innovation, the Speech-Assisted Chatbot for Medical Infectious Disease Prediction, promises to transform healthcare delivery. By leveraging AI-driven algorithms and natural language processing, it facilitates quick diagnosis and prescription generation through spoken interactions with patients. This simplifies and accelerates procedures, reduces error risks, improves patient care standards, saves valuable time for healthcare professionals, and enhances service availability, all contributing to better outcomes and more efficient healthcare processes.. Key Words: chatbot, NLP, Machine learning, Medical diagnosis, Speech, Artificial Intelligence etc.

An innovative ensemble model based on deep learning for predicting COVID-19 infection

Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events.

Medical Disease Prediction using Machine Learning Algorithms

Abstract: There is a growing importance of healthcare and pandemic has proved that healthcare is an important aspect of an individual life. Most of the medical diagnoses require going to the doctor and fixing appointments for a consultation and sometimes to get accurate disease indications we have to wait for blood reports also we have to travel long distances to seek doctor consultation. When we are not feeling well the first thing we do is to check our temperature to get an estimate or baseline idea of our fever so we can consult our doctor if the temperature is high enough similarly a medical disease prediction application can be used to get a baseline idea of disease and can indicate us whether we should take immediate doctor consultation or not, or at least start some home-remedies for the same to find temporary relief. Combining machine learning with an application interface to interact with users provides opportunities for easy interaction with the users with the machine learning model to get more accurate predictions. Sometimes people feel reluctant to visit a hospital or consult a doctor for minor symptoms but there are cases where these minor symptoms may be indications of severe health problems hence medical disease prediction maybe useful to get a baseline prediction or estimation of disease in such cases. Keywords: machine Learning, prediction, medical diagnosis, healthcare

A dynamic preserving-based WSN framework for medical disease prediction

A dynamic preserving-based WSN framework for medical disease prediction

A dynamic preserving-based WSN framework for medical disease prediction

A dynamic preserving-based WSN framework for medical disease prediction

Multi-Disease Prediction Based on Deep Learning: A Survey

In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI’s research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some basic deep learning frameworks and some common diseases, and summarizes the deep learning prediction methods corresponding to different diseases. Point out a series of problems in the current disease prediction, and make a prospect for the future development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates the high correlation between deep learning and the medical field in future development. The unique feature extraction methods of deep learning methods can still play an important role in future medical research.

Medical disease prediction using Grey Wolf optimization and auto encoder based recurrent neural network

Big data development in biomedical and medical service networks provides a research on medical data benefits, early ailment detection, patient care and network administrations.e-Health applications are particularly important for the patients who are unfit to see a specialist or any health expert. The objective is to encourage clinicians and families to predict disease using Machine Learning (ML) procedures. In addition, diverse regions show important qualities of certain provincial ailments, which may hinder the forecast of disease outbreaks. The objective of this work is to predict the different kinds of diseases using Grey Wolf optimization and auto encoder based Recurrent Neural Network (GWO+RNN). The features are selected using GWO and the diseases are predicted by using RNN method. Initially the GWO algorithm avoids the irrelevant and redundant attributes significantly, after the features are forwarded to the RNN classifier. The experimental result proved that the performance of GWO+RNN algorithm achieved better than existing method like Group Search Optimizer and Fuzzy Min-Max Neural Network (GFMMNN) approach. The GWO-RNN method used the medical UCI database based on various datasets such as Hungarian, Cleveland, PID, mammographic masses, Switzerland and performance was measured with the help of efficient metrics like accuracy, sensitivity and specificity. The proposed GWO+RNN method achieved 16.82% of improved prediction accuracy for Cleveland dataset.

An Improved Particle Swarm Optimization based classification model for high dimensional medical disease prediction

Recently, machine learning techniques have become popular and widely accepted for medical disease detection and classification on high dimensional datasets. Classification models is one of the essential model in machine learning models for medical disease prediction due to its fast processing speed, high efficiency and noisy datasets. Traditional machine learning models are failed to estimate the disease patterns with high true positive rate due to large number of features and data size. In this paper, a novel particle swarm optimization based hybrid classifier was implemented for medical disease prediction with high dimensions. The main objective of the feature selection based hybrid classifier is to classify the high dimensional data for large medical feature set. Proposed filtered based hybrid classifier is usually designed and implemented to improve the medical prediction rate on high dimensional data. In this work, we have used different ensemble learning models such ACO+NN, PSO+ELM, PSO+WELM to analyze the performance of proposed model(IPSO+WELM). Experimental results are evaluated on different types of medical datasets including lung cancer, diabetes, ovarian, and DLBCL-Stanford. Performance results show that proposed IPSO+WELM with ensemble model has high computational efficiency in terms of true positive rate, error rate and accuracy.

Genetic algorithm pruning of probabilistic neural networks in medical disease estimation

A hybrid model consisting of an Artificial Neural Network (ANN) and a Genetic Algorithm procedure for diagnostic risk factors selection in Medicine is proposed in this paper. A medical disease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Network models were assessed in terms of their classification accuracy concerning medical disease prediction. A Genetic Algorithm search was performed to examine potential redundancy in the diagnostic factors. This search led to a pruned ANN architecture, minimizing the number of diagnostic factors used during the training phase and therefore minimizing the number of nodes in the ANN input and hidden layer as well as the Mean Square Error of the trained ANN at the testing phase. As a conclusion, a number of diagnostic factors in a patient's data record can be omitted without loss of fidelity in the diagnosis procedure.