Implementation of XpertMalTyph: An Expert System for Medical Diagnosis of the Complications of Malaria and Typhoid

This research provides a system that integrates the work of data mining and expert system for different tasks in the process of medical diagnosis, and provides detailed steps to the process of reaching a diagnosis based on the described symptoms and mapping them with existing diagnosis available on the web or on a cloud of medical knowledge based, aggregate these data in a fuzzy manner and produce a satisfactory diagnosis of the persisting problem. The mobile phone interface would make the system user-friendly and provides mobility and accessibility to the user, while posting updates and reading in details the steps that led to the decision or diagnosis that is reached by the K-mean and the fuzzy logic inference engine. The achieved results indicate a promising diagnosis performance of the system as it achieved 90% accuracy and 92.9% F-Score.

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“…They suggest the implementation of the ES with artificial neural networks (ANN). The XpertMalTyph was executed in Java Expert System Shell [9].…”

Section: Related Workmentioning

confidence: 99%

A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

Al-Ars

Al-Bakry

2019

TELKOMNIKA

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“…But all these aforementioned have become the order of the day in most hospitals; especially in developing countries. Patients cannot get to see the doctor at the right time and there are some patients that find it difficult communicating their health issues and the doctor in turn has no time to keep asking the probable questions Fatumo et al, 2013. Another issue that has posed a problem in medicine in the developing world is the inadequate infusion of technology to aid the activities involved in the practice.…”

Section: Accuracy Of Malaria and Typhoid Coinfectionmentioning

confidence: 99%

“…Another issue that has posed a problem in medicine in the developing world is the inadequate infusion of technology to aid the activities involved in the practice. Fatumo et al, 2013.…”

Section: Accuracy Of Malaria and Typhoid Coinfectionmentioning

confidence: 99%

A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

Aminu¹,

Ogbonnia²,

Shehu³

2016

IJMSC

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High costs of medical equipment and insufficient number of medical specialists have immensely contributed to the increment of death rate especially in rural areas of most developing countries. According to Roll Back Malaria there are 300 million acute cases of malaria per year worldwide, causing more than one million deaths. About 90% of these deaths happen in Africa, majorly in young children. Besides malaria when tested; a large number is coinfected with typhoid. Most often, symptoms of malaria and typhoid fevers do have common characteristics and clinicians do have difficulties in distinguishing them. For instance in Nigeria the existing diagnostic systems for malaria and typhoid in rural settlements are inefficient thereby making the result to be inaccurate and resulting to treatment of wrong ailments. Therefore in this paper, a predictive symptoms-based system for malaria and typhoid coinfection using Support Vector Machines (SVMs) is proposed for an improved classification results and the system is implemented using Microsoft Visual Basic 2013. Relatively high performance accuracy was achieved when tested on a reserved data set collected from a hospital. Hence the system will be of a great significant use in terms of affordable and quality health care services especially in rural settlement as an alternative and a reliable diagnostic system for the ailments.

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“…Xpertmaltyph: An Expert System for Medical Diagnosis of the Complications of Malaria and Typhoid was developed by Fatumo et al in [15]. This research was motivated by the dearth of medical experts in the developing world which has subjected a large percentage of its populace to preventable ailments and deaths, the few medical experts always opt to practice in the urban cities.…”

Section: Review Of Related Literaturementioning

confidence: 99%

“…A bacterial called salmonella typhi (s.typhi) is responsible for typhoid fever. S.typhi may be spread by consuming contaminated water, beverages and food, after which the bacteria enter the intestines and then the bloodstream, where they may spread to other body parts [15]. Typhoid fever has a global presence, over 17 million people affected annually by typhoid fever and 600,000 succumb to it [12].…”

Section: Introductionmentioning

confidence: 99%

Computer Aided Diagnostic Systems for Managing Typhoid Fever: A Review of Diagnosis Techniques

Oguntimilehin¹,

Abiola²,

Olatunji³

2015

IJCA

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Implementation of XpertMalTyph: An Expert System for Medical Diagnosis of the Complications of Malaria and Typhoid

Cited by 15 publications

References 7 publications

A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

A Predictive Symptoms-based System using Support Vector Machines to enhanced Classification Accuracy of Malaria and Typhoid Coinfection

Computer Aided Diagnostic Systems for Managing Typhoid Fever: A Review of Diagnosis Techniques

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