# An Optimized Neuro_Fuzzy Based Regression Trees for Disease Prediction Framework
## 2 Related Work  
Various authors and researchers have highlighted the same study shown in the research article. Some of the significant contributions are discussed here in the section below:  
An illness could be a pathological condition during which the part of the body or organ and therefore the system might not work correctly. Mostly, it is due to some infection, hereditary or genetic disorders. Heart disease, also called artery disease, is caused by the artery and veins becoming slim. Coronary failure is mainly due to blood flow circulation to the guts. Several techniques are projected to predict guts illness; however, their design and calculation are incredibly advanced. [PERSON] et al. [24] presented the various pre-processing strategies for neural network-based mostly heart condition prediction to beat this drawback. PCA, in collaboration with LDA, defines pre-processing techniques, which are used to reduce the spatial property of the dataset, resulting in a classification accuracy of 94.53% for the detection of cardiac problems.  
In recent times, the primary issue the cloud-based system faces is merging data before cutover and data migration. To overcome this issue, the cloud-based structure must be integrated with the IoT. Thus, [PERSON] et al. [25] proposed a new technique named FETCH, combining deep learning and cloud-based IoT. This system helps in analyzing the healthcare of humans. This framework uses a fog bus which illustrates the system's latency, execution time, bandwidth, and accuracy. Moreover, this technique monitors the heart rate and other body functions. However, no prevention or control technique is discussed.  
Artificial intelligence (AI)-based solutions are gaining significant traction in the development of medical diagnostic systems. Many researchers use different decoding methods; however, they're troublesome to handle and sophisticated evaluation. To achieve this, [PERSON] et al. [26] planned state-of-the-art AI techniques in heart condition designation, and therefore, AL techniques, such as ANN, algorithmic rules, and mostly symbolic-based systems, are preponderantly applied within the assignment for heart condition prediction.  
Recently, a massive volume of health data has been developed due to recent breakthroughs in information and communication technologies (ICT) and online assistance services. The analysis of disease prediction networks (DPSS) has attracted several academics worldwide thanks to advances in machine learning methodologies. For this purpose, [PERSON] et al. [27] developed a hybrid reasoning-based privacy-aware malady prediction network for diagnosing problems. Although DPSS facilitates good aid services, information security and privacy are still crucial complex problems to be self-addressed.  
T1 DM is a kind of diabetes that occurs when the body's immune system assaults and kills insulin-producing cells in the exocrine gland. To keep T1 DM patients alive, an insulin pump or daily injections are essential for internal secretion supplied by Associate in Nursing. WHO should also carefully synchronize internal secretion dosages with uptake and physical activity day and night. Despite rigorous monitoring, they are at risk of dangerously high and low blood glucose levels (BGL), which can lead to catastrophic repercussions and life-threatening hypoglycaemic episodes. [PERSON] and [PERSON] et al. [28] modeled prognosis management for type 1 diabetic rats using a neural network-based model.  
The following summarizes one of our research's most important contributions.  
* Incorporate a sensor into the human body.
* Using the Internet of Things, collect real-time medical information about a patient.
* Using GARIC architecture, process and classify information gathered about the patient.
* To use data mining techniques such as regression trees and optimize utilizing ALO to evaluate and forecast any disease or disorder in its early stages.
* Receive notifications through SMS, email, etc.
* To give healthcare solutions based on the Internet of Things at any time and location.