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Proactive Frequently monitoring heart rate and condition of health

Proactive Healthcare

Tofayel Ahmad

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Minnesota State University Mankato




Proactive strategies advance quality of life, of patients
with various diseases (chronic diseases, obstructive pulmonary disease (COPD)
and Congestive Heart Failure(CHF)). Proactive health care technology can be
used “just-in-time” to motivate, sustain and behavior change. The objective of
this research paper is to progress chronic disease management, early detection
and early treatment whenever possible. Most of the exacerbations remain
unreported and treatment delayed, resulting death and hospitalization
oftentimes. A randomized clinical trial is described to fix upon integration of
self-management education and proactive remote disease observing system
proposed to enhance the outcomes.

monitoring system proposed in this research paper is to detect the diseased and
preventing (chronic disease and obstructive pulmonary disease (COPD)) using
sensors, typically biometric sensors implanted on the body or worn on the body.
Frequently monitoring heart rate and condition of health by implementing three
layers monitoring system. Based on interviews and monitoring automatically
using sensors, a patient in home or on the way information can be inferred from
sensors. For the first two layers, Decision Support System(DSS) and Ubiquitous
sensors and DSS are developed using Random Forest Algorithm for early detection
(Guidi, Pollonini, Dacso, Ladanza, 2015). The result is tremendous success in
achieving accuracy, sensitivity and specificity in a 10-fold cross-validation
where 71.9 % in expecting the number of decompensations and 81.3% in severity
assessment (Guidi E 2015). The method and system are developed pose a promising
difference in healthy life style and health care for CHF patients and chronic

Keywords:  Decision Support
System, self-management, Decision Support System, CHF


The Effects on Proactive Health Research

healthcare is not a new term that has been introduced in health care but it’s a
term that introduce a new method to actively manage health care.


 Chronic disease is a
state of health that the disease last long and persistent in its nature. If a
disease lasts longer than 3 months would be a chronic disease. Most commonly
found chronic disease are Congestive
Heart Failure (CHF or HF), Asthma, Cancer, Cystic Fibrosis, Diabetes, ALS (Lou
Gehring’s Disease). Often pose a high risk and results hospitalization and
death. Most chronic diseases found in aging people require rigorous care and
effort to get the patient back in good health. Definitive health care has not
been evolved and the treatment applied is often misleading. Hospitalization is
very expensive in USA and high amount of cost involved in chronic diseases is
related to hospitalization. Chromonic diseases do not develop in a very short
time. They developed over the period and early detection is necessary to
prevent it causing death and re-hospitalization. Noncommunicable diseases
(NCDs) is prevailing over aging and young population in recent years. According
to Global Health Observatory (GHO) by 2030 NCD related death will be increased
where 63% people were death out of 57 million global death in 2008.  According to the Centers for Disease Control
and Prevention (CDC), 7 out of 10 deaths every year due to chronic diseases in
USA. Most chronic disease is preventable if proper treatment applied.
Application of the medicine has some side effects. (Samadi, Sep,12,2012). The
most common side effects among heart disease patient due to use of aspirin are
gastrointestinal bleeding, nausea, vomiting, diarrhea, hair loss, loss of
appetite, fatigue, among cancer patients due to chemotherapy (Samadi,
Sep,12,2012). The life become stagnate because of all this effects and
diseases. A demand creates here for early detection of the causes and to
develop a treatment strategy to prevent al this from happening.




the reactive form of healthcare, prevention comes latter as focus on curing the
chronic diseases. The format has been profoundly formulated into our lifestyle
and healthcare system. We don’t seem to take early measurement and protection
until the sickness kicks off. Majority of the chronic patients often wait too
long for urgent help and the waiting time have never been pleasant for doctors
and patients. A definitive method of measurement strategy requires analyzing all
possible variables, care setting and make a strategy plan for family and
patient to perform self-management (Guidi, et all). Periodic follow-up on CHF
patients abate death from CHF through regular phone calls and home visits
according to recent research by Cochrane Collaboration. That’s not the best strategy
to abate the death but comparatively produce better result than the conventional
treatment strategy.

a nut-shell, proactive self-management is a compound strategy to implement
where patients and family requires active part in the time of treatment. Lack
of constancy and patient results huge number of avoidable hospitalization. A
solution proposed by this research paper three-layer system build on two
clinical layers and one patient layer. Decision Support System (DSS) is an
effective machine learning engine can analyze patient data collected from
sensors (fabric sensors, warble sensors) for self-measurement through the
layers. A comprehensive data structure is required to produce quality predicted
data from the monitoring model. A clinical decision system is a instigation of
DSSs, sensor devices, telemedicine infrastructures and cross-platform
interfaces. The proposed system compliant with HIPAA cloud architecture that
enable centralize collection and secure connection between layers. Extensive monitoring
taken in the layer 1 and require patient to visit hospital les frequently.
All physical measurement’s data sync to the all layers through telematic
infrastructure. In the 2nd layer, a nurse every 1-2 week visit patient’s home
to collect the data using measuring kit and tablet computer.













Layer 3 is self-management system
where patient perform his/her own care. The acquisition process required
parameters such as electrocardiogram(EKG), heart rate(HR), pulse transit
times(PTT), weight and bio impedance(bioZ) performed at home Guidi Gab.


According to the second research paper, Decision Support
System (DSS) is an information system help in decision making by using Random
Forest Algorithm.  Decision Support
System is not a total solution for clinical health care system. Clinical
Decision Support System (CDSS) can play vital role in health care system.
Clinical decisions systems are stand on clinical guidance and evidence-based
rules formulated form medical science. Intelligent decision support systems
(IDSS) is introduced to assist doctors taking decision related to diagnosis and

Decision Tree algorithm can produce accurate analytical results and
human readable rules for doctors by using the data derive from diagnosis of
diseases. In decision tree algorithm, trees are split into node and the
decision trees are made of randomly selecting features subset for each decision
node improves prediction precision. The Decision Tree structure 1 is similar
of tree structure divided into branches (node). Root node starts from the tree
root connected to the branches and leaf’s called leaf node. Classified date
derived from patient history and principal rules, each subsets of tree node
falls into algorithm structure to make decision in a very short time.









Decision Support System (IDSS)

Intelligent behavior can be displayed the result of leering
and reasoning through machine based rule system, knowledge-based systems and
neural network systems 2 (Ali, et al). Artificial neural network demonstrate mathematical
model of biological neural networks allows taking decision such as uncertainty
and recommendation. A systematic process is required to manage health data. Reconstruct
information into intelligence data that can be human readable and stakeholders
in a system. A health care knowledge cycle (Patel et al) 4 can be compared to
information and processing model.










IDSS can identified pattern for
patients based on principle rules developed by medical science, it can also
raise warning flags when any pattern reaches the thresh hold. Chronic dieses
management will be more efficient. In a time of epidemic, IDSS can predict
future spread in a region by analyzing the date derived from the same patient
pattern. IDSS can also identify drug-drug interactions real-time. A slight
change in patient health alarm doctors to prevent further application of the


of an IDSS Model

A rule-generator algorithm is developed depending on data
mining of a knowledge base (KB) with an artificial neural network (ANN). The
system is developed can learn repository knowledge through datamining. Data
mining identify patient pattern extracted from domain knowledge. The system
acquire knowledge and retain the knowledge for future prediction and to solve
problems. The architecture includes 1 a decision-oriented data repository,
2 inductive algorithm, 3 knowledge base, 4 an intelligent advisory











Multiple database is buildable by
the help of data mining algorithms and applicable in knowledge bases. A
predictive model is generated using neural network.










The proposed Intelligence Decision Support System and
3-layer model system developed could change the total health care drastically.
Proactive healthcare is opposed to conventional healthcare system. Which does
pose uncertainty to the implication of the system as it is more expensive to
build and more time consuming as well as unnatural to our mind setup but IDSS
model may become fundamental healthcare system in future because of it can
produce significant changes in healthcare system. Saving a patient comes priority
to every system.

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Stevenson LW: Rehospitalization for heart failure: predict or prevent?
Circulation 2012, 126(4):501-6, Jul..
Guidi, G.,
Pollonini, L., Dacso, C. C., & Iadanza, E. (2015, September 04). A
multi-layer monitoring system for clinical management of Congestive Heart
Failure. Retrieved December 08, 2017, from https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-15-S3-S5
Gr?bczewski, K.,
& Duch, W. (2003). Forests of Decision Trees. Neural Networks and
Soft Computing, 602-607. doi:10.1007/978-3-7908-1902-1_92


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