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Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier—is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively. [ABSTRACT FROM AUTHOR]

It has been estimated that nearly half of the world's population is at the risk of contracting malaria with sub Saharan Africa being the most risky area. The existing frontline malaria control interventions are not only expensive but also become ineffective owing to the emergence of insecticide and drug resistance. It calls for an innovative approach in terms of potential and reliable vaccine as an additional tool. Over centuries, the public health experts have been actively engaged to formulate a safe, affordable and potential malaria vaccine and accordingly a notable achievement has also been attained. However, many challenges are required to be flagged immediately and effectively to devise an ideal prophylactic malaria vaccine. Therefore, the global community has to remain waiting quite a few more years to build a wannabe malaria-free world in the near future. [ABSTRACT FROM AUTHOR]

With the exception of Central America and the Caribbean, falciparum malaria resistant to the 4-aminoquinoline chloroquine is now found throughout the tropics. Using allelic exchange, mutation K76T in lysosomal membrane protein PfCRT of the intraerythrocytic parasite has been proved to be responsible. An additional residue change (A220S) appears necessary but not sufficient. The effectiveness of more hydrophobic 4-aminoquinolines like desethylamodiaquine in chloroquine resistance in Africa is possibly associated with the more hydrophobic PfCRT 72CVIET76 haplotype, in contrast with SVMNT and CVMNT. Resistance to 4-aminoquinolines in the presence of modified PfCRT is enhanced by residue changes in another lysosomal membrane protein, PGH-1, a member of the multidrug-resistance associated 'ATP-binding cassette' (ABC) protein group. PGH-1 mediates resistance to arylaminoalcohols, like quinine and other cinchona alkaloids and to the highly hydrophobic arylaminoalcohols (HHAAs) such as mefloquine. In quinine resistance, PGH-1 residue-changes are similar to those seen in enhanced 4-aminoquinoline resistance. In contrast, increased copy number of wild-type PGH-1 is found in clinical resistance to the HHAA mefloquine. There is also evidence of some interaction between the arylaminoalcohols and PfCRT. In the case of quinine and its diastereomer quinidine, sterospecificity has been demonstrated. Although the main genetic determinants of resistance are now clear, there is some evidence for the involvement of other genes. [ABSTRACT FROM AUTHOR]