The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke. Most of the literature work is based on the MRI and CT scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes. In India where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes. Hence, this problem has motivated us to conduct the study presented in this paper. A noninvasive approach for the early diagnosis of the strokes is proposed. The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of EEG. Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed EEG data. This paper is proposing time series-based approaches such as LSTM, biLSTM, GRU, and FFNN that can handle time series-based predictions to make useful decisions. The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with 95.6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. The experimental outcome is able to measure the brain waves to predict the signs of strokes. The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients.

Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome‐wide protein interaction (PPI) data is growing as high‐throughput experiments become more common. The aim of this research is that it provides a dual‐tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer‐based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three‐dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double‐tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two‐dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual‐tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure.

Global mortality rates are high due to heart disease, it is crucial to early analyzing the risk factors of prediction and diagnosis are crucial. Decision trees are a reliable machine-learning method for this problem. Four decision tree algorithms CART (Classification and Regression Trees), C5.0, M5P (Model Tree with Linear Models in Leaves), and XGBoost are tested for heart disease risk factor prediction. An extensive dataset of heart disease risk factors was used to evaluate decision tree algorithms. To assess model performance rigorously, the study used data preprocessing, feature selection, and stratified cross-validation. The results from the experiments were significant. Despite its simplicity, the CART algorithm predicted heart disease risk factors accurately. The C5.0 algorithm handled missing data well, a common healthcare dataset issue. M5P, which integrates linear models into leaf nodes, improves interpretability without compromising accuracy. XGBoost was the best model, predicting risk factors with 99.32% accuracy. The result shows the algorithm's durability and suitability for complex medical diagnostic tasks. Decision tree algorithms predict heart disease risk factors effectively and clearly. XGBoost was the best decision tree algorithm due to its accuracy and reliability. This technology's precision suggests it could help doctors make quick, accurate diagnoses, potentially saving lives. This study highlights the importance of using machine learning and decision trees to improve heart disease risk assessment and patient care. Further research could examine the integration of XGBoost and other decision tree models into clinical practice to improve heart disease detection and treatment.