Machine Learning Intelligence based Guiding System

GRD Journals | Global Research and Development Journal for Engineering | International Conference on Innovations in Engineering and Technology (ICIET) - 2016 | July 2016

e-ISSN: 2455-5703

Machine Learning Intelligence based Guiding System P. Bini Palas Assistant Professor Department of Electronics and Communication Engineering Easwari Engineering College, Ramapuram, Chennai 600089, India Abstract Pregnancy is the most significant stage of a woman’s life. Natural labour is boon to a pregnant woman. Women who have natural childbirths are extremely empowered and feel much more confident. The system being designed aims at providing guidance towards safe natural labour. The key concept is to gather details about the individuals and maintain the database in the cloud and segregate the users into major groups based on various measurable medical parameters. Queries are made by the individuals, which is then answered by the users of the system. The suggested solution is commented by the medical practitioners and posted to be availed by others, on the group. Machine learning intelligence, decides the solution that is specific for a particular group.

Thus the system guides the individuals towards safer natural labour, in case of emergency.

Keyword- Machine learning intelligence, medical parameters, cloud, Internet of Things __________________________________________________________________________________________________

I. INTRODUCTION Pregnancy is a boon to a woman. The gestation is the time during which the offspring develops inside a woman. This is just over nine lunar months, where each month is about 29½ days. The time period from the onset of labour to child birth is the most crucial period. And here awaits the scene of whether it’s a normal labour or C-section. The recovery time for a normal delivery is much shorter than that of a C-section. C-section has few complexities such as too much blood loss, injury to mother and baby, late surgical complications. The proposed system guides an individual towards safer normal labour.

The gestation period of 280 days is categorized into nine stages. The changes felt by different individuals at different levels are grouped under each stage. The personal data of the users are held in a database and the individual is related to the stage where she is. When the user posts a query on the complication being felt, it is viewed by all the users. The machine, by now maps the individual with the exact level in the particular stage. The remedies received will also be mapped with the exact level.

Once the justification from the physician is received the reply is given to the individual over the group. The individual thus finds an immediate solution before reaching the hospital.

II. CLOUD DATABASE A. Common Discomforts Morning sickness is a common symptom of early pregnancy that usually goes away by the end of the first three months. Morning sickness or nausea can happen at any time of the day and is caused by changes in hormones during pregnancy. The ligaments naturally become softer and stretch to prepare for labour. This can put a strain on the joints of the lower back and pelvis, which can cause backache. Many women experience some rather unpleasant conditions. Maintaining a healthy diet and doing regular exercise can help make a bit less uncomfortable. Cramps, swelling and varicose veins are some of the most well known issues women experience during pregnancy [7]. Getting plenty of rest should help to alleviate the symptoms. These symptoms may vary from one person to another.

B. Data Collection Each and every day of the gestation period gives a different experience for each individual. The gestation period of humans is about 40 weeks (280 days). The experience had, varies from one to other. To attain the exact solution for the arising medical issues, the individuals experiencing the similar issues are categorized into different groups. These issues when sorted on a primary level can guide the individual towards a safe normal delivery. Bodily changes occur in the individual week by week. The changes may range from minor to a major level such as nausea, constipation to vaginal bleeding, signs of labour etc. Having an eye on all these aspects, a system with machine learning intelligence is to be designed to provide the solution to the complications.

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Machine Learning Intelligence based Guiding System (GRDJE / CONFERENCE / ICIET - 2016 / 092)

Once the individual logs into the system the particulars related to the individual is collected and stored in the cloud database. The needed diagnosable medical factors and other personal data related to the individual such as age, date of conception etc. are entered in the database. Depending on the collected data the individuals are segregated into different groups [5] .

III. ISSUES DURING GESTATION A. Placenta Previa Having a low-lying placenta is noticed among many women during their course of pregnancy. Survey on 309 patients having low lying placenta was held. The mean distance from the os was 13.4mm. Of 172 patients with follow up scans, 143 (83.1%) demonstrated complete resolution, defined as located > 2 cm from the os. The placenta was located 1-2 cm from the os for the remaining 29 cases (16.9%). Thus, all 172 patients were eligible for a trial of normal labour, resulting in a resolution rate of 100%.

Placenta previa is an obstetric complication in which the placenta is inserted partially or wholly in the lower uterine segment. It is a leading cause of antepartum haemorrhage (vaginal bleeding). It affects approximately 0.4-0.5% of all labours. In the last trimester of pregnancy the isthmus of the uterus unfolds and forms the lower segment. In a normal pregnancy the placenta does not overlie. If the placenta does overlie the lower segment, as is the case with placenta previa, it may shear off and a small section may bleed. Women with placenta previa often present with painless, bright red vaginal bleeding. This commonly occurs around 32 weeks of gestation, but can be as early as late mid-trimester. This bleeding often starts mildly and may increase as the area of placental separation increases. Previa should be suspected if there is bleeding after 24 weeks of gestation. The only mode of recovery is bed rest with no related medication. Lack of awareness on this may lead to a C-section.

B. Onset of Labour Painful contractions or tightenings that may be irregular in strength and frequency, and may stop and start is considered as the symptom of onset of labour. Rupture of membrane with a gush or a trickle of amniotic fluid alarms the start of labour. Major issue is to identify the onset of labour and to differentiate between the Braxton Hicks (false pain) and the true labour contractions.

IV. ENGINEERING APPROACH A. Machine Learning Intelligence The field of Machine Learning is organized around the development and analysis of learning systems to improve performance in a predetermined set of tasks and the investigation and simulation of human learning processes. Machine Learning system can classify the work to be done based on underlying learning strategies, representation of knowledge and application domain. Over the years, machine learning has been pursued with varying degrees of intensity [6]. With relative history of this discipline the proposed system makes intelligent picking of queries and solution to the same; by correlating between the conditions and parameters in common for both the parties. The available parameters are heterogeneous to different groups. Comparison and differentiation of the different parameters based on the group to which the individual belongs to, is done in a scrutinized manner with the aid of machine learning intelligence [1].

B. Cloud Computing The existing system for patients’ vital data collection requires a great deal of human work to collect, input and analyze the information. These processes are usually slow and error-prone, introducing a latency that prevents real-time data accessibility.

This scenario restrains the clinical diagnostics and monitoring capabilities. Another solution to automate this process is using sensors attached to existing medical equipments that are inter-connected to exchange service[2]. This system is based on the concepts of utility computing and wireless sensor networks. The information becomes available in the cloud from where it can be processed by expert systems and/or distributed to medical staff. The proof-of-concept design applies commodity computing integrated to legacy medical devices, ensuring cost-effectiveness and simple integration.

Also cloud Computing provides functionality for managing information data in a distributed, ubiquitous and pervasive manner supporting several platforms, systems and applications. Implementation of a mobile system that enables electronic healthcare data storage, update and retrieval using Cloud Computing can be achieved. The mobile application is available using Android operating system and provides management of patient health records and medical images [3].

C. Internet of Things The revolution in the era of computing is changing in comparison to traditional desktop. Internet of Things (IoT) is basically connecting two or more devices together to form a network and thereby allowing the connected devices to connect between them for data transfer. IoT has become quite common everywhere and this revolves around the increased machine to machine communications. It is built on cloud computing and various sensors interfacing. Many objects that surround the human users will

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Machine Learning Intelligence based Guiding System (GRDJE / CONFERENCE / ICIET - 2016 / 092)

be on the network in one form or in another form in the Cloud Computing and Internet of Things framework. Cloud Computing and Internet of Things are two different technologies; that are to take up their vital part in our day today life. Internet of Things and Cloud was visualized separately, but now it’s not the case. The need for integration of Cloud and Internet of Things is growing at its peak.

V. FUNCTIONAL REPRESENTATION The system utilizes the data from the cloud database and maps the query and the defined solution to be provided to the user.

Fig. 1: General Representation of the System

A. Intel Edison The Intel Edison is a tiny Computer-on-module offered by Intel as a development system for wearable devices and Internet of Things devices. It is the latest microcontroller that is highly useful for low power applications and it has easier interface with many of the sensor. This micro controller has 20 digital I/O pins including 4 PWM pins; 6 analog inputs. It also has a micro SD card holder to connect a SD card for storing data externally.

Fig. 2: Intel Edison Board

B. Cloud Computing and Storage Cloud computing is defined as the form of network that is obtained by connecting various computers together and sharing the information from one server to other devices that are attached to it. The important features of cloud computing should include the following features:  On-demand self service  Broad network access  Resource pooling  Rapid elasticity  Measured optimal service Cloud storage is basically storing of data on a large database and accessing them online. Thus, it is nothing but any other storage device that is available in large quantities. Various organizations and people buy or lease storage capacity from users to store user organization or application data. The cloud used for this idea is IBM Bluemix.

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Machine Learning Intelligence based Guiding System (GRDJE / CONFERENCE / ICIET - 2016 / 092)

C. Intel XDK – App Development Android application has become an important field of area and is almost used everywhere. The coding for this purpose is done by using Intel XDK. The Intel XDK is a development kit that is created by Intel to create applications for mobile phones and tablets using web technologies like HTML, Java Script and CSS. The Apps are usually compiled online via the Cordova platform for making cross-platform apps. After creating the application, the result can viewed by using the mobile phone or through the emulator window that is provided by this software [4]. Any app consists of the front end and the back end. The front end is the one that is visible to user and the backend is the layer where the program coding is present.

Fig. 3: App Development

The system relies on the basic tools needed for cloud computing and implementing the IoT based design onto a mobile application.

VI. PROPOSED SYSTEM – OUTLINE The system highly relies on the machine learning concept; the system itself provides the needed solution. The various personal details of the individual are fed to the system. The features like age, date of conception, blood group etc. are collected and maintained in the database. This will in turn be managed by the machine learning part. Here the individuals are segregated into major groups based on the similarities existing between them. Let the groups be named as A, B, C etc. Now better knowledge is had on the issues being faced in the nine different stages of pregnancy. Each stage will have a different problem, the count increases from a few to several towards the end of the third trimester. Correlation is done between the different groups and the 9 stages at different levels.

Fig. 4: Proposed System

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Machine Learning Intelligence based Guiding System (GRDJE / CONFERENCE / ICIET - 2016 / 092)

Once an issue is identified, the individual posts it. All the registered individuals will be able to view it and post a solution on the same. As soon as the comments are received the machine maps it to, from where the solution is received. For example; it checks, from which group A or B or C, is the solution got. It is then mapped with the individual who has posted the query (Check to which group the individual belongs). The queries will be answered only with the solution given by another individual of the same group. Immediately, after the mapping is over, the solution will be presented to a physician for review.

Once the provided solution is validated and found to be meticulous it is posted by the system, to be accessed by the individuals.

This presents a thoughtful idea to work out with before moving to the health centre.

VII.

APPLICATION OUTPUT

The real time working of the system will be the success behind the proposed system. The developed module to feed the input, i.e. the user end to enter the query is shown in fig. 5. The raised query is entered in the particular field to enable the system.

Fig. 5: Input Entry Field

As soon as the input is fed to the system, the functioning starts and executes as per the one described and the user gets benefited.

VIII. CONCLUSION C-section births are found to be in a rise, all over the world. The WHO published guidelines regarding caesarean rates in 1985 which was revised in 1994. The guidelines state that the proportion of caesarean births should range between five and fifteen per cent. Both the developed and the developing countries are not showing a satisfactory mark for the rates. The level of caesarean section (CS) is well above the WHO (1985) mentioned fifteen per cent mark for many of the countries, and it is increasing over the time. Though the estimates of CS rates in India is 7.1 per cent in the year 1998 and there is 16.7 per cent change in the rates annually in India, which is one of the highest among the countries.

Minor issues like nausea, constipation, giddiness etc. can easily be rectified. Water level in the third trimester, low lying placenta identified during the second trimester, hypertension at the end of pregnancy are few factors that may lead to a C-section.

In many cases, an overweighing fetus may also pose to be a reason for a C-section. Having a close monitoring of the food habits during the third trimester can promote a safe normal labour. In the same sense, all healthy habits and diets can also be given to the individuals.

REFERENCES [1] Babu, S. M.; Lakshmi A.J.; Rao, B.T. "A study on cloud based Internet of Things: Cloud IoT", Communication Technologies (GCCT), 2015 Global Conference.

[2] Rolim .C.O, Koch .F.L, Westphall .C .B, ―A Cloud Computing Solution for Patient’s Data Collection in Health Care Institutions‖, eHealth, Telemedicine and Social Science, 2010.

[3] Upkar Varshney, "Pervasive Healthcare", IEEE Computer Magazine vol. 36, no. 12, 2003, pp. 138-140.

[4] Lenz, R.; Reichert, M. IT support for healthcare processes—Premises, challenges, perspectives. Data Knowl.

Eng. 2006,61, 39–58.

[5] Li, M.; Yu, S.; Ren, K.; Lou, W. Securing Personal Health Records in Cloud Computing: Patient -centric and Finegrained Data Access Control in Multi-owner Settings. In Proceedings of the 6th International ICST Conference on Security and Privacy in Communication Networks (SecureComm 2010), Singapore, 7 –9 September 2010; pp. 89–106.

[6] Machine Learning, Neural and Statistical Classification - Michie D, Spiegelhalter D.J, Taylor .C.C [7] website: www.healthtalk.org

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