Coronary Artery Disease (CAD) is a form of heart disease primarily influenced by lifestyle choices. Despite preventative measures available to mitigate CAD risks, a significant proportion of the population remains unaware of its severity and consequently neglects necessary precautions. As a result, the influence of CAD continues to rise. This project aims to curb CAD cases by developing an early warning detection and educational accessible to the general population, leveraging Machine Learning and Data Visualization technologies. Research indicates that while Coronary Artery Disease can be mitigated through a lifestyle shift towards healthier living, the risk remains due to factors such as age and natural health deterioration.

Alzheimer's disease (AD) is a neurodegenerative disease that affects cognitive abilities (thinking and memory etc) primarily among the elderly, due to which collective cognitive skills deteriorate, ultimately leading to death. Early detection of Alzheimer's disease is crucial for determining appropriate therapeutic options. This research investigates the use of a Deep Convolutional Neural Network (CNN) for detecting Alzheimer's disease. Due to similar brain patterns and pixel intensities, CNN demonstrates promising results in diagnosing AD through automated feature extraction and characterization. Deep Learning algorithms are designed to perform automated feature extraction and categorization of input image datasets. In this study, a two-way classifier categorizes each image as either Healthy Control (HC) or Alzheimer's disease (AD). Experiments were carried out with the MIRIAD dataset, and the accuracy of disease classification into binary categories was evaluated. The recorded results of CNN with 4- and 5 -layer architectures confirms the effectiveness of the proposed method for AD detection.

Cardiovascular disease (CVD) continues to contribute significantly to preventable deaths and avoidable disability worldwide. Prediction and prevention are of utmost importance in the support of public health. Machine learning and deep learning algorithms have emerged as powerful tools to improve the accuracy of diagnosis, prognosis, and treatment of cardiovascular disease. By employing these technologies, medical professionals can gain valuable insights into the risk factors associated with CVD. The focus of this research is to classify and predict cardiovascular diseases using techniques such as support vector machines, ensemble methods, decision trees, random forests, and neural networks. The effectiveness of these algorithms is evaluated based on metrics including accuracy, sensitivity, specificity, area under the curve (AUC), and F1 score. Results show that support vector machines and ensemble methods offer superior accuracy, while neural networks exhibit higher sensitivity and specificity in predicting cardiovascular diseases.

In recent years, lung cancer incidents are very high with equally high mortality rate. The main reason for fatal incidences is the late diagnosis and confirmation of the disease at an advanced stage. Identification of the disease at an early stage using lung Computed Tomography (CT) offers tremendous scope for timely medical intervention. The article illustrates the use of deep transfer learning-based pre-trained models for the diagnosis of Non-Small Cell Lung Cancer (NSCLC). The datasets were chosen from Chest CT Scan Images and the Lung Image Database Consortium (LIDC), containing over 3,179 images depicting three NSCLC types, namely, normal, adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. The process is designed to measure the accuracy of NSCLC detection with an experimental dataset using approaches with and without pre-processing of lung images. The transfer learning models use deep learning and produce good results in prediction and classification. The image dataset was first handled by the convolutional neural networks DenseNet121, ResNet50, InceptionV3, VGG16, Xception, and VGG19. As a second phase, input images were subjected to contrast/brightness enhancement using Multi Level Dualistic Sub Image Histogram Equalization (ML-DSIHE). Enhanced images were further processed using shape-based feature extraction. Finally, those features input to CNN models and the results recorded. Among these models, VGG16 achieved the highest accuracy of 81.42% using the original dataset and 91.64% with the enhanced dataset. The performance of these two approaches was also evaluated using Precision, Recall, F1-Score, Accuracy, and Loss. It is confirmed that VGG16 gives highly reliable accuracy when trained upon enhanced images.

INTRODUCTION: Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting movement control, highlighting the importance of timely detection and intervention to improve patient quality of life. However, accurate diagnosis remains challenging due to its similarity with other neurological conditions, leading to a 25% rate of inaccurate manual diagnoses. Convolutional Neural Networks (CNNs) offer a promising solution for medical image classification and analysis, capable of learning complex patterns in images. In this study, we introduce an innovative automated diagnostic model using CNN that gives an appropriate output about if the person is diagnosed with PD or not.OBJECTIVES: The study aims to develop an automated diagnostic model using CNNs to accurately diagnose PD. By leveraging the Parkinson Progression Markers Initiative (PPMI) dataset, which provides benchmarked MRI images of PD and healthy controls, the model seeks to differentiate between PD and non-PD cases.METHODS: A Convolutional Neural Network (CNN) is a deep learning algorithm that is suitable for medical image classification and analysis as they are able to learn complex patterns in images and identify the hidden patterns and trend of data. We have used VGG16 and ResNet50 pretrained CNN models to achieve high accuracy and prediction.RESULTS: These models collectively achieved an outstanding accuracy rate of 97%. To validate our model performance, we test our model by applying various algorithms and activation functions such as EfficientNetB0, EfficientNetB1 and softmax, sigmoid, and ReLu respectively.CONCLUSION: This research introduces an innovative framework for the early detection of Parkinson’s disease using convolutional neural networks. Our system demonstrates remarkable capability to identify subtle patterns indicative of PD in its early stages.

INTRODUCTION: DNA microarray has become a promising means for classification of various cancer types via the creation of various Gene Expression (GE) profiles, with the advancement of technologies. But, it is challenging to classify the GE profile since not all genes contribute to the presence of cancer and might lead to incorrect diagnoses. Thus an efficient GE data analysis for microarray cancer data classification using Exponential Sigmoid-Deep Belief Network (ES-DBN) is proposed in this work.OBJECTIVES: The study aims to develop an efficient GE data analysis using Exponential Sigmoid-Deep Belief Network (ES-DBN) for microarray cancer data classification.METHODS: The proposed methodology starts with pre-processing to compact data. Afterward, by utilizing Min-Max feature scaling technique, the pre-processed data is normalized. The normalized data is further encoded and feature ranking is performed. The subset values are selected using Cauchy Mutation-Coral Reefs Optimization (CM-CRO) in feature ranking. The feature vector is calculated by Pearson Correlation Coefficient based GloVe (PCC-GloVe) algorithm since different subsets return the same fitness value. Statistical and Biological validations take place after feature vector calculation. Lastly, for effective classification of the type of cancer, the vector features obtained are fed to ES-DBN.RESULTS: The outcomes of the proposed technique are evaluated with various datasets, which exhibited that the proposed technique performed well with the Ovarian cancer dataset and outperforms other conventional approaches.CONCLUSION: This study presents a comprehensive methodology for efficiently classifying cancer types using GE profile. The proposed GE data analysis using ES-DBN shows promising results, highlighting its potential as a valuable tool for cancer diagnosis and classification.

INTRODUCTION: Cardiovascular diseases, particularly coronary artery disease (CAD), present a global health challenge, necessitating effective detection and diagnosis methods for early intervention. Various machine learning and deep learning approaches have emerged, utilizing diverse data sources such as electrocardiogram (ECG) signals and clinical features to enhance CAD detection. Additionally, circadian heart rate variability (HRV) has been explored as a potential diagnostic marker for CAD severity. This research aims to contribute to the burgeoning field of medical AI and its application in cardiology.OBJECTIVES: This study seeks to develop a Comprehensive Coronary Artery Disease Detection System integrating real-time heart rate monitoring and CAD prediction via an Android application. The objectives include seamless data transmission, efficient cloud-based data management, and the utilization of AI models, including ANNs, CNNs for ECG images, and hybrid models combining clinical and ECG data, to improve early CAD detection and management.METHODS: The system architecture involves integrating key sensors, an Arduino microcontroller, a Bluetooth module, and AI models to facilitate early CAD detection. An Android application complements the system, offering heart rate monitoring and CAD prediction using various data sources. Cloud computing is employed for efficient data management and analysis.RESULTS: The developed system successfully integrates cutting-edge technology to enhance CAD detection, achieving accurate and efficient results in real-time heart rate monitoring and CAD prediction.CONCLUSION: The Comprehensive Coronary Artery Disease Detection System, leveraging AI and cloud computing, contributes to proactive health monitoring and informed decision-making in CAD management and prevention, thereby addressing a critical need in cardiovascular health care.

As a carrier of college students' health status monitoring, portable intelligent terminal APP, the study of its APP application analysis not only provides a new way for college students' extracurricular physical exercise, guides college students to actively participate in extracurricular physical activities using intelligent terminal APP software, but also promotes college students' physical health monitoring and enhancement in various aspects. Aiming at the current portable terminal APP college students' health monitoring application analysis method research exists low precision, real-time poor and other problems, through the analysis of the basic functional framework and functional characteristics of the portable intelligent terminal APP, the establishment of the portable intelligent terminal APP analysis index system applied to college students' health monitoring, combined with the heuristic optimisation algorithm and the improvement of deep learning algorithms, the construction of the marine predator based heuristic optimisation algorithm and the attention mechanism to improve the gating control loop. Combining the heuristic optimisation algorithm and the improved deep learning algorithm, we construct the portable intelligent terminal APP application analysis method for college students' health monitoring based on the marine predator heuristic optimisation algorithm and the attention mechanism improved gated recurrent unit neural network. Through simulation analysis, the results show that the proposed method meets the real-time requirements while improving the prediction accuracy of the portable smart terminal APP application analysis method, and significantly improves the efficiency of portable smart terminal APP analysis.

With the development of network technology and intelligent application platforms, the "Three Precision" applet as a method of individual intervention for students' physical fitness can not only enable students to obtain the improvement of physical fitness and lifelong sports habits, but also establish a new bridge of cooperation between home and school. The analysis method of student physical fitness individual intervention assessment is affected by a variety of factors such as the framework design of the WeChat applet platform and the subjectivity of the intervention, which leads to the inefficiency of the student physical fitness individual intervention assessment method. To address this problem, we analyse the mode and content of students' physical fitness individual intervention based on the "Three Precision" applet, extract the feature vectors of students' physical fitness individual intervention, construct a system of students' physical fitness individual intervention assessment indexes, and establish a method of students' physical fitness individual intervention assessment based on big data technology and WeChat applet by combining the mushroom propagation optimization algorithm and convolutional neural network. Individual intervention assessment method based on big data technology and WeChat applet. The effectiveness and robustness of the proposed method are verified by using the data recorded in the "Three Precision" applet as the input data of the model. The results show that the proposed method meets the real-time requirements and improves the prediction accuracy of the individual intervention assessment method, which significantly improves the efficiency of the individual intervention assessment of students' physical fitness.

INTRODUCTION: Muscle warm-up plays a fundamental role before developing any physical activity because it allows the body to prepare to perform better in physical activity, being a process that is carried out through a series of moderate intensity exercises that result in an increase gradual reduction of muscle and body temperature, avoiding possible injuries or muscle pain. Therefore, muscle warm-up is an essential activity mainly in those sports where greater force is exerted on the legs, being the part of the body where injuries such as ankle sprains or knee injuries are commonly seen that lead to painful and uncomfortable injuries for students-athletes.OBJECTIVES: Develop a thermal image processing system to monitor the muscle warm-up of students prior to their sports activities to evaluate the state of the muscle warm-up of the leg part and prevent damage or injuries, as well as the indication of requiring another additional muscle warm-up to determine a correct muscle warm-up.METHODS: The proposed method involves the use of thermal images to monitor muscle warm-up before and after physical activity. In addition, the use of MATLAB software to analyze the images and compare the status of muscle warm-up.RESULTS: Through the development of this proposed system, its operation was appreciated with an efficiency of 95.97% in monitoring the muscle warm-up of the students prior to their physical activities achieved through image processing.CONCLUSION: It is concluded that the proposed system is effective in monitoring muscle warm-up and preventing injuries in student-athletes.