Simple Summary Timely and accurate detection of cardiovascular diseases is critical to reduce the risk of myocardial infarction. This article proposes a methodology using machine learning, neuro-fuzzy and statistical methods to predict cardiovascular diseases. Our results show that the proposed methodology outperformed well known approaches, reaching a high prediction accuracy greater than 90%. Our methodology helps medical doctors to enhance diagnosis, quality of healthcare and efficacious prescriptions, decreasing the time for exams and minimizing expenses in clinical practice. Abstract Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient’s age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods.

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Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods

ABSTRACT For successful prognosis of cardiovascular diseases (CVDs), an early and quick diagnosis is essential. Heart disease and strokes are the predominant causes and account for more than 80% of CVD deaths, whilst one-third of these deaths occurs prematurely in people under 70 years of age. For CVD diagnosis, patients need to show an elevated level of biomarkers in the blood sample associated with severe pain in the chest, and diagnostic electrocardiogram (ECG). The majority of CVD patients making CVD diagnosis difficult for physicians show a surprisingly normal ECG pattern. Artificial intelligence techniques can radically improve and optimize CVD outcomes. AI has the potential to provide novel tools and techniques to collect and interpret data and make faster and more accurate decisions reducing hospitalization cost, thereby increasing the quality of life. AI has also improved medical knowledge by unlocking clinically relevant information from the voluminous and complex data received from various resources. This paper reviews the present biosensors and describes various AI techniques, which can effectively be used for early and accurate detection of CVD, thereby improving cardiac care.

https://doi.org/10.1080/08839514.2022.2031816

Cardiac Diagnosis with Machine Learning: A Paradigm Shift in Cardiac Care

Leading a healthier life in this modernized world is the greatest gift. One of the major health issues that are very predominant among majority of human community is heart related diseases. This is mainly due to the food habits, life style, and lack of health awareness. Precise and exact diagnosis, prediction is indispensable for such heart diseases because even the slight neglecting leads to critical health complexity results in person demise. So, appropriate system is highly needed to detect the symptoms of such heart diseases in the earlier stage so that suitable treatment must be given to ensure the proper functioning of heart. Machine learning and deep learning technology provides positive acceleration to the health sector by its accurate and precise prediction of such diseases at the earlier stage from the massive health care data. The key focus of this article is to explore the major symptoms of such heart diseases, successful machine learning and deep learning models for the prediction of such diseases and available public dataset. 

ML &amp; DL Methodologies in Heart Disease Prediction: Brief Analysis

Health is a state of total physical, mental, and social wellbeing. chatbots have been applied to this industry frequently and in a variety ofways in the past, there is still room for more inventive uses. Healthcareconversational AI use cases are flexible and may be tailored to the industry. Patients might use them to gain additional knowledge about their disease, the therapies that are available, or even their insurance coverage. Because research has shown that healthcare chatbots can improve patient satisfaction and significantly reduce wait times, many healthcare organisations are considering incorporating them into their operations. Chatbots for healthcare can be used for a number of purposes, such as monitoring, anonymity, personalization, in-person involvement, and more. In this case study, the user's input on the patient's symptoms will be used to determine the patient's likely ailment type. According on the type of sickness, precautions will be suggested, and the patient will be sent to a doctor who specialises in that field. A sequential model was utilised to extract the text's symptoms, and the KNN method was then applied to predict the patient's ailment type.1

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Conversational AI Chatbot for HealthCare

Heart diseases severely affects how people live. A serious condition that affects people of all ages worldwide. Diabetes and heart disease patients have significantly higher mortality rates. A lot of lives may be saved if heart disease could be predicted earlier, especially in people who have diabetes. Advanced machine learning algorithms assist in providing an earlier prediction and accurate method for classifying and predicting cardiac disease. In order to predict cardiac disease, this paper presents a thorough overview of both traditional and modern machine learning algorithms. Furthermore, it suggests a cardiovascular disease prediction model that is both efficient and accurate, notably for diabetes patients. This approach suggested uses the support vector machines and random forests. The experiments were performed, and the findings demonstrate how effective the suggested framework. 

A Methodology For Early Prediction and Classification of Heart Diseases in Diabetic Patients With Machine Learning Techniques

Cardiovascular diseases are one of the most vital causes offatality. Cardiovascular disease prediction is a critical challenge in the area of clinical data analysis. Machine learning and Neural Networks are more promising in assisting decide and predict from the massive data produced by healthcare. We have noted different features had used in recent developments of the machine learning model. In this paper, we proposed machine learning techniques to predict cardiovascular disease using features. BMI is one of the highlighting features we used for prediction. BMI is important in predicting cardiovascular disease. The main focus of the article isthe effect ofBMI onthe prediction of cardiovascular disease. The model has proposed with different features as well as regression and classification techniques. We conclude that BMI is a significant factor while predicting cardiovascular disease.

Cardiovascular Disease Prediction Using Machine Learning Models

The most common psychiatric disorder is Clinical Depression. More than fifteen percent of people undergo an incident of major depression sometime during their life. There is an increase in depressive disorders and other-regarding symptoms in past years, which leads to the importance of early detection of depression. Detection of depression can be automated by analyzing a person's behaviour and emotions. The depressive disorder influences auditory speech qualities, facial expressions, thinking, and some cardiovascular activities; hence depression can be recognized by analyzing such factors and also trying to find out which method is more suitable and reliable. Through preprocessing, features of different signals are extracted and further handled to develop a machine learning model. Neural Networks are a faster and efficient way to analyze depression. This survey briefs about different techniques and machine learning models for depression detection.

A Survey: Feature Extraction Techniques and machine learning models for Depression Analysis

Smart technology is being used in every possible field to make our life easier. To connect humans with the virtual world, computing devices like Smart Glasses are vital. Smart Glasses are very useful in medical, entertainment, education, and many other fields. We proposed smart glasses to assist a person with a Monocular vision in carrying out his day-to-day activities. Understanding depth is the challenge faced by such a person. To overcome this problem, we designed glasses using the Convolution Neural Network and YOLOv3. Recording videos and AR are one of the most important features of Wearable smart glasses. Our glasses assist in measuring the depth of objects (Distance between camera and objects) using a camera module and providing real-time support. It will be useful while judging the distance between approaching barriers. In congested or crowded areas, these glasses help individuals to collect the surrounding information by measuring the distance between different objects.

Smart Glasses to Assist Monocular Vision to Estimate Depth

Road conditions and obstacles indication system is a system that can be added to a vehicle to indicate possible events that can cause sudden deceleration on the road to avoid rear-end collisions and for improving road safety while driving. Normally every vehicle is equipped with a tail light to indicate the brakes, but this indication is dependent on the application of brakes. Due to this, they are not helpful in various cases that contain sudden deceleration. Most of the rear-end collisions occur when the driver of the vehicle is Unable to predict the behavior of the speeding of the preceding vehicle. The tail light is a manual option for this problem, but Road conditions and Obstacle Indication System provides an automatic option. This system contains simple architecture that includes the camera sensors, processing unit, and an indicating device. To prevent rear-end collisions, the indication system helps the following driver to make judgments based on Road conditions and the type of obstacle along with its intensity in front of the preceding driver. This system can use to increase the safety of road traffic by using more relevant signals for indications of obstacles. According to the invention, the indicator comprises an indication of obstacles like potholes, road bumps, and objects appearing in front of the vehicle including its intensity, here intensity is considered as distance between obstacle and vehicle. A significant purpose of the Road conditions and Obstacles Indication System (RCOIS) is to indicate that the preceding driver may slow down or stop the vehicle.

Aditya Mhaske

Papers

Cardiovascular Disease Prediction Using Machine Learning Models

A Survey: Feature Extraction Techniques and machine learning models for Depression Analysis

Smart Glasses to Assist Monocular Vision to Estimate Depth

Road Conditions and Obstacles Indication and Autonomous Braking System