Design and Development of An Intelligent System For Medical Diagnosis Based on Multi- Dimensional Analysis

Abstract

The advancement of healthcare prediction systems has revolutionized the medical field, enabling to predict and prevent diseases severity, improve patient care, and enhance healthcare efficiency. This requires proper study of historic data in the related field and thorough analysis. Greater emphasis is laid on relevance of live data rather than repository data available in scholarly database. Again, the causes of a disease may vary geographically due to distinct living conditions or environmental conditions. At the same time, the ability of a medical practitioner to decipher information out of diagnosis procedure followed will be limited by his expert knowledge or experience. It is in such situations that a reliable accurate prediction system based on Artificial Intelligence (AI) comes as an assisting tool to the medical fraternity in conflict resolution. An AI-based diagnostic system will definitely help the medical expert in arriving at remedial solution, since knowledge base contained in it is based on sound design. The prediction system attempted in the present work consists of two stages. In the first stage, prediction system was developed for classification of undifferentiated fever symptomatic disease. The motivation of good results at this stage led to the development of full-fledged end- to-end predictive system for identification and classification of coronary artery disease (CAD), with consideration of electrocardiogram (ECG) and treadmill test electrocardiogram (TMT-ECG, stressed ECG) signals. It is then validated with angiography results. Accurate diagnosis of undifferentiated fever symptomatic disease at the earliest is a challenging task necessitating extensive diagnostic tests. The aim of the present study was to apply Artificial Intelligence (AI) algorithm using temperature information for the prediction of major categories of diseases among undifferentiated fever symptomatic disease cases. Illnesses like tuberculosis, non-tubercular bacterial infection, dengue fever, and non-infectious diseases have regular manifestations of fever symptoms. The present work uses only temperature data of the patient being referred in predicting the nature of fever symptomatic disease, with the highest degree of accuracy, instead of several self-defined parameters over an interval of time. This was an observational study carried out in tertiary care hospital and validated with thehelp of experienced physicians. Back-propagation algorithm was used to train the network. A good relation was found between the target data set and output data set, purely based on the observed 24 hrs. continuous tympanic temperature of the patients. An accuracy of 99% was achieved from the Artificial Neural Network (ANN) prediction model. Prediction model with different classifiers (logistic regression, decision tree classifier, k-nearest neighbor’s classifier, linear decrement analysis, Gaussian Naive Bayes classifier, and Support Vector Machine) were experimented for optimization. The optimized prediction model deals with lesser time intervals and shows good performance of results when it is combined with additional medical parameters which may be considered during medical testing. A result of predictive system defines with a good classifier adaptation will show a strong performance in identification of fever-symptomatic diseases. Accuracy score and other salient parameters describe the complete picture of the system. No other investigation has ever been carried out so far taking temperature as the only parameter in classification of diseases achieving an accuracy of as high as 99.9%. Based on the success attained here, a more complicated problem is taken up for investigation related to coronary artery disease. Coronary artery disease (CAD) is one of the major cardiovascular diseases and is a cardiac condition where plaque formed in arteries leads to death worldwide. The identification of CAD in the traditional approach needs a report of ECG, TMT ECG, Pharmacological test, and echocardiogram. The confirmation of CAD leads to the next stage of cardiac catheterization. An accurate prediction system that can detect the existence of CAD with an initial test like an ECG or TMT ECG report can assist doctors during periodic health monitoring of patients. It may be challenging and time-consuming to visually assess the ECG signals. Identification of abnormal ECG morphology, especially in low amplitude curves may be prone to error. Initially, an image processing method has been developed and implemented for the extraction of data from ECG and TMT-ECG reports. The 12 lead TMT-ECG report provides cardiac information of abnormality under medication. This information plays a vital role in automated cardiac analysis. Any small discontinuity in the ECG/TMT-ECG images will be patched up by the developed method. The data extraction method involves scanning of ECG and TMT-ECG images, masking,binarization, and morphological operation, etc. These extracted data are compared with the available output of commercial software (IM2GRAPH) In addition to data extraction, a part of the algorithm based on hybrid method is used to identify and classify important major features namely P, Q, R, S, T, PQ segment, QRS complex, QT segment, and ST segment. A convolutional neural network model is developed which works on the data extracted from ECG signals (one-dimensional data). The developed Convolutional Neural Network (CNN) architecture deals with single-lead and multi- lead (12 Lead) ECG and TMT-ECG data effectively. The highlight of the CNN system developed is that entire data is collected from the clinical lab of a renowned neighboring hospital. The automated computer-assisted system helps in the detection of CAD with an accuracy of 99%. The study also focused on developing a prediction system for CAD disease based on raw and filtered, single-lead and twelve-lead ECG signal images (two-dimensional), by passing data extraction. The algorithm results are compared with transfer learning algorithms. The novelty of the work is highlighted by the fact that the prediction accuracy of the developed algorithm, with a single lead and twelve lead ECG or TMT ECG signals (accuracy of approximately 93.5% for single lead and 94% for twelve lead) is much higher compared to transfer learned algorithms. The developed model exhibited better accuracy with lesser number of layers compared to deeper pre-trained algorithms. Further improvement is achieved by developing a novel multi-headed model which deals with both one-dimensional data and two-dimensional data simultaneously. This hybrid deep multi-headed model is built with a combination of two prediction models which work parallelly. The outcomes of these models are concatenated at the end part of model before flowing to the output layer. This process helps to extract and collect more featuristic information related to disease with all possibilities during prediction. To generalize this methodology, it is further tested over a repository dataset and has shown good performance and acceptable results. For good accessibility, a user-friendly Graphical User Interface (GUI) is developed based on proposed algorithm to support healthcare experts in classifying CAD ECG signals without much effort. The prototype model which is developed can be tested with a still larger dataset before implementation for clinical usage.