LovHealth: LovIoTech Healthcare IoT-Cloud Platform for Patient Care Based On Diagnosis System with Fuzzy Logic and Machine Learning Approach

Abstract

Innovation in the health sector in the world is advancing, and there are increasingly more challenges that need to be addressed. The current problem is that most of the world's population is afflicted with diseases, such as heart disease, that go undiagnosed because of their subtle symptoms and the difficulty and cost of diagnostic techniques. Many struggle to access adequate medical treatment and expensive diagnostic testing. The only function of medical devices is to monitor health data, and no diagnostic procedure aids patients. As an application of system diagnosis in IoT, this research develops a diagnosis system, provides treatment information, and an appointment system in health data processing. This study uses machine learning and fuzzy logic approaches to offer convenience to patients in self-diagnosis processes monitored by doctors. To optimize the IoT product, a fuzzy logic experiment was tested to produce a diagnosis with three variable parameters: stress level, oxygen, and temperature. These three variables will diagnose disease symptoms experienced by users based on the measurement of four data sensors: heart rate, oxygen saturation, galvanic skin response, and body temperature. In the machine learning approach, the experiment conducted trials with several Decision Tree, KNN, SVM, Random Forest, and Logistic Regression models to forecast cardiovascular disease diagnosis. The Confusion Matrix results show that the approach with the highest value is Random Forest, with an Precision of 81.5%, Recall of 83.7%, F1-Score 82.5%, and Accuracy of 82.6%. This indicates that diagnosing heart disease can be more efficient using the Random Forest approach. With these two approaches, patients can be facilitated in carrying out the diagnosis process independently and remotely without the need to come to the hospital. Doctors can easily monitor and provide treatment with each patient's electronic health record platforms. This is expected to increase the level of optimal health services.