## 1 Introduction
Machine learning (ML) is a subset of artificial intelligence which has already proved its effectiveness in the diagnosis and prediction of diverse incurable diseases at an earlier stage [6]. The purpose of using machine learning in the health sector is to increase the efficiency of medical treatment by detecting if a patient has some specific disease based on his/her health pattern. Developing such a model can be very resourceful in the case of rescue missions because a delay in detecting health complications during emergencies causes obstacles to initiating necessary treatment paths, resulting in a high mortality rate. Besides, the traditional diagnosis methods applied in rescue situation has several drawbacks like [7]:  
1. Lengthy diagnostic process which involves many observations and a wide range of questions
2. Requirement of high expertise from rescuers3 Decision-making based on the available data requires a lot of time and can come out wrong
4. Due to inadequate data, no correct diagnosis can be done, and a patient is just referred to the hospital  
Here, we must not forget that the experience of physicians to diagnose such critical complications is undeniable and often irreplaceable. The use of ML algorithms is only to make the diagnostic process smoother, more reliable and more precise. The objective of this research is two fold. Firstly, we develop detection models that are time efficient and provide good accuracy to overcome the challenges caused by traditional diagnostic methods as explained above. Different machine learning algorithms are chosen for the detection model to test their performance in detecting neurological complications of rescue patients. Further, their time to infer complication probability from an unknown health pattern is considered. Prior to model development, analysis of neurological rescue cases is done with different data analysis techniques. The analysis facilitates extracting new features to create a training dataset, which is an essential part of machine learning algorithms. This paper will provide an overview of the detection models developed with the dataset from the rescue station, Siegen-Wittgenstein of their rescue missions from 2012 till 2021. Seven different algorithms including neural network are used to develop the detection models and later a performance comparison in terms of accuracy and time efficiency is provided. There are a handful of examples using machine learning in the diagnosis of health complications like cardiovascular, neurological or cancer detection using X-ray, Magnetic resonance imaging (MRI), computed tomography angiogram (CTA), radiology imaging tests or pathology reports. The novelty of this research is, here the dataset considered for the detection model is generated only from health vitals collected in rescue operations and primary observation from rescuers. No medical test data or pathology were used because it is not practical in emergency situations to wait for hours to process the test results. Even in many rescue cases, those medical tests cannot be conducted due to the limitation of test equipment or time-contingency. The power of machine learning to derive predictive models from substantial amounts of rescue data with minimal or, even in some cases, without providing prior knowledge of the data can play a vital role in this scenario. The model developed is accelerated in a Xilinx FPGA to find out the model's suitability in real-world situations. FPGA shows tremendous potential in the case of accelerating neural network because of its feature of reconfiguration ability of hardware architecture down to bit-level, fast computational capability and doing massive parallel operations. Hence, in rescue situations where a quick inference is highly expected after passing the health data of emergency patients, FPGA is an innovative solution. However, the purpose of this paper is not to provide the technical aspects of machine learning model acceleration in FPGA or its extensive implementation.