Monitoring Cardiovascular Problems in Heart Patients Using Machine Learning.

The World Health Organization reports that heart disease is the most common cause of death globally, accounting for 17.9 million fatalities annually. The fundamentals of a cure, it is thought, are important symptoms and recognition of the illness. Traditional techniques are facing many challenges, ranging from delayed or unnecessary treatment to incorrect diagnoses, which can affect treatment progress, increase the bill, and give the disease more time to spread and harm the patient's body. Such errors could be avoided and minimized by employing ML and AI techniques. Many significant efforts have been made in recent years to increase computer-aided diagnosis and detection applications, which is a rapidly growing area of research. Machine learning algorithms are especially important in CAD, which is used to detect patterns in medical data sources and make nontrivial predictions to assist doctors and clinicians in making timely decisions. This study aims to develop multiple methods for machine learning using the UCI set of data based on individuals' medical attributes to aid in the early detection of cardiovascular disease. Various machine learning techniques are used to evaluate and review the results of the UCI machine learning heart disease dataset. The proposed algorithms had the highest accuracy, with the random forest classifier achieving 96.72% and the extreme gradient boost achieving 95.08%. This will assist the doctor in taking appropriate actions. The proposed technology will only be able to determine whether or not a person has a heart issue. The severity of heart disease cannot be determined using this method.

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification.

Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals' visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques' results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.

Fuzzy Logic-Based Systems for the Diagnosis of Chronic Kidney Disease.

Kidney failure occurs whenever the kidney stops to operate properly and would be unable to cleanse or refine the bloodstream as it should. Chronic kidney disease (CKD) is a potentially fatal consequence. If this condition is diagnosed early, its progression can be delayed. There are various factors that increase the likelihood of developing kidney failure. As a consequence, in order to detect this potentially fatal condition early on, these risk factors must be checked on a regular basis before the individual's health deteriorates. Furthermore, it lowers the cost of therapy. The chronic kidney or renal disease will be recognized in this work utilizing fuzzy and adaptive neural fuzzy inference systems. The fundamental purpose of this initiative is to enhance the precision of medical diagnostics used to diagnose illnesses. Nephron functioning, glucose levels, systolic and diastolic blood pressure, maturity level, weight and height, and smoking are all elements to consider while developing a fuzzy and adaptable neural fuzzy inference system. The output variable describes a specific patient's stage of chronic renal disease based on input factors such as stage 1, stage 2, stage 3, stage 4, and stage 5. The outcome will show the present stage of a patient's kidney. As a result, these methods can assist specialists in determining the stage of chronic renal disease. MATLAB software is used to create the fuzzy and neural fuzzy inference systems.

Face mask recognition system using CNN model.

COVID-19 epidemic has swiftly disrupted our day-to-day lives affecting the international trade and movements. Wearing a face mask to protect one's face has become the new normal. In the near future, many public service providers will expect the clients to wear masks appropriately to partake of their services. Therefore, face mask detection has become a critical duty to aid worldwide civilization. This paper provides a simple way to achieve this objective utilising some fundamental Machine Learning tools as TensorFlow, Keras, OpenCV and Scikit-Learn. The suggested technique successfully recognises the face in the image or video and then determines whether or not it has a mask on it. As a surveillance job performer, it can also recognise a face together with a mask in motion as well as in a video. The technique attains excellent accuracy. We investigate optimal parameter values for the Convolutional Neural Network model (CNN) in order to identify the existence of masks accurately without generating over-fitting.

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach.

Breast cancer is a strong risk factor of cancer amongst women. One in eight women suffers from breast cancer. It is a life-threatening illness and is utterly dreadful. The root cause which is the breast cancer agent is still under research. There are, however, certain potentially dangerous factors like age, genetics, obesity, birth control, cigarettes, and tablets. Breast cancer is often a malignant tumor that begins in the breast cells and eventually spreads to the surrounding tissue. If detected early, the illness may be reversible. The probability of preservation diminishes as the number of measurements increases. Numerous imaging techniques are used to identify breast cancer. This research examines different breast cancer detection strategies via the use of imaging techniques, data mining techniques, and various characteristics, as well as a brief comparative analysis of the existing breast cancer detection system. Breast cancer mortality will be significantly reduced if it is identified and treated early. There are technological difficulties linked to scans and people's inconsistency with breast cancer. In this study, we introduced a form of breast cancer diagnosis. There are different methods involved to collect and analyze details. In the preprocessing stage, the input data picture is filtered by using a window or by cropping. Segmentation can be performed using k-means algorithm. This study is aimed at identifying the calcifications found in bosom cancer in the last phase. The suggested approach is already implemented in MATLAB, and it produces reliable performance.