Diagnosing malaria from some symptoms: a machine learning approach and public health implications

Okagbue, Hilary I.; Oguntunde, Pelumi E.; Obasi, Emmanuela C. M.; Opanuga, A. A.

doi:10.1007/s12553-020-00488-5

Cited by 8 publications

(4 citation statements)

References 157 publications

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“…[ 61 ]. A decision support system that implements these results especially for low resources settings is recommended [ 62 , 63 ].…”

Section: Discussionmentioning

confidence: 99%

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Okagbue,

Ijezie,

Ugwoke

et al. 2023

Heliyon

Self Cite

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“…[ 61 ]. A decision support system that implements these results especially for low resources settings is recommended [ 62 , 63 ].…”

Section: Discussionmentioning

confidence: 99%

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Okagbue,

Ijezie,

Ugwoke

et al. 2023

Heliyon

Self Cite

View full text Add to dashboard Cite

“…Te discovery of the apicoplast in malaria parasites has opened up novel prospects in antimalarial drug discovery and development because the organelle is absent in mammals, essential throughout the lifecycle of the parasite, and it possesses nonmammalian metabolic processes, which are critical for parasite growth and development [4,7]. Several reports have indicated that the apicoplast is an excellent drug target, and more work is currently being done to develop therapeutic interventions with novel mechanisms of action or combination therapies to abrogate apicoplast-resident metabolic processes and diminish the current problem of antimalarial drug resistance [9][10][11]. Here, we provide an overview of the metabolic processes occurring in the apicoplast and discuss the organelle as a viable antimalarial target in light of current advances.…”

Section: Introductionmentioning

confidence: 99%

Apicoplast-Resident Processes: Exploiting the Chink in the Armour of Plasmodium falciparum Parasites

Mamudu,

Tebamifor,

Sule

et al. 2024

Advances in Pharmacological and Pharmaceutical Sciences

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The discovery of a relict plastid, also known as an apicoplast (apicomplexan plastid), that houses housekeeping processes and metabolic pathways critical to Plasmodium parasites’ survival has prompted increased research on identifying potent inhibitors that can impinge on apicoplast-localised processes. The apicoplast is absent in humans, yet it is proposed to originate from the eukaryote’s secondary endosymbiosis of a primary symbiont. This symbiotic relationship provides a favourable microenvironment for metabolic processes such as haem biosynthesis, Fe-S cluster synthesis, isoprenoid biosynthesis, fatty acid synthesis, and housekeeping processes such as DNA replication, transcription, and translation, distinct from analogous mammalian processes. Recent advancements in comprehending the biology of the apicoplast reveal it as a vulnerable organelle for malaria parasites, offering numerous potential targets for effective antimalarial therapies. We provide an overview of the metabolic processes occurring in the apicoplast and discuss the organelle as a viable antimalarial target in light of current advances in drug discovery. We further highlighted the relevance of these metabolic processes to Plasmodium falciparum during the different stages of the lifecycle.

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“…The study by Meulen [3] noted that companies had increased their investment in big data from 58-75% due to the vast potential it has to offer. Sectors such as the e-commerce space [4], education [5], manufacturing [6], public health [7], weather prediction [8], diabetes prediction (public health) [9], ISSN: 2088-8708  Application of machine learning in cement price prediction … (Adedeji Olushola Afolabi) 5215 earthquake prediction [10], and oil and gas [11] are participating in the big data advanced analytics to assist in its decision making and provide deep insight. Specifically, there is a need to apply big data in planning and decision-making about building materials.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning in cement price prediction through a web-based system

Afolabi

Abimbola²

2022

IJECE

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Cement is one of the most common building materials in the construction industry. Simultaneously, its price fluctuation can affect the success or failure of the construction project's performance. The study aimed to develop a web-based platform that uses machine learning algorithms on historical data of cement prices, petrol prices, diesel prices, interest rate, and exchange rate to predict future prices of cement products. The web-based learning platform was developed using hypertext markup language (HTML), cascading style sheet (CSS), MySQL, and hypertext preprocessor (PHP). For building a reliable machine learning model, python language was used to train the system. The front end, the back end, and the machine learning model were integrated with a flask python framework. A system block diagram was designed to show the web-based learning platform's interfaces. The web-based learning platform's system implementation led to the login page, the home page, database page, and cement price analytics interface. In training the machine learning model to make reliable cement price predictions, the study obtained an 80% fitted model in the linear regression. The web-based machine learning platform was able to predict the prices of cement. The rationale behind the machine learning prediction shown by the scatter plot diagram revealed that the cement increases by 250 naira biannually.

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Diagnosing malaria from some symptoms: a machine learning approach and public health implications

Cited by 8 publications

References 157 publications

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Apicoplast-Resident Processes: Exploiting the Chink in the Armour of Plasmodium falciparum Parasites

Application of machine learning in cement price prediction through a web-based system

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