An Intelligent Smart healthcare system for patient monitoring based on CNN machine learning algorithm

Diseases are spreading swiftly among people in the modern society, and it is challenging to diagnose illnesses in older people with diabetes, heart disease, kidney disease, and other health challenges. They affect the neurological system and result in vision loss in addition to the diseases indicated above. The proposed method provides a machine learning technique that uses convolution neural network (CNN) classifiers to detect diseases for patients earlier, offering an advantage over linear regression, KNN and decision tree approaches. This algorithm has good accuracy, precision, and recall compare to KNN, Linear regression, Decision tree methods. The recommended system is a trustworthy health monitoring system with sufficient intelligence to leverage the IoT to keep an eye on the patient without human intervention. By using these technologies, it gathers significance information about the patient, including blood pressure, temperature, heart rate, and ECG and uses that information to send an emergency warning via an Android phone to the patient’s physician and patients relative, along with the patient’s present situation. The proposed system allows the consultant as well as caregiver to observe their patient from wherever in the globe. Smart sensors are used by this proposed system to generate real time data that is to be gathered from various sensors and is to be sent to a cloud for analysis and statistical safeguarding before being used. In the recommended patient monitoring system approach, the parameters pertaining to the patient’s health are tracked health metrics with a Raspberry Pi. The Raspberry Pi act as server and sends data to the website then the consultant and caregivers can use laptops, tablets, and smart phones to check a patient’s health issues where ever by using their IP address. Doctors and family members will receive an automatic SMS alert if any of these parameters deviate from normal. This study makes use of a Raspberry Pi Board as an Internet of Things gadget to read patient health parameters and interface with different sensors. These health metrics are then sent to the cloud. The caregiver and doctor have cloud access to these values. If the doctor is unable to visit the patient in the hospital, the proposed system will be a helpful tool for quickly giving prescriptions and keeping an eye on the health of the patient from a distance.